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Victor Chernozhukov

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Daron Acemoglu & Victor Chernozhukov & Iván Werning & Michael D. Whinston, 2021. "Optimal Targeted Lockdowns in a Multigroup SIR Model," American Economic Review: Insights, American Economic Association, vol. 3(4), pages 487-502, December.

    Mentioned in:

    1. Some Covid Links
      by Don Boudreaux in Cafe Hayek on 2021-12-04 12:32:56
  2. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2019. "Distributional conformal prediction," Papers 1909.07889, arXiv.org, revised Aug 2021.

    Mentioned in:

    1. Interval Prediction
      by Francis Diebold in No Hesitations on 2019-10-12 19:16:00

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography of Economics:
  1. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.

    Mentioned in:

    1. > Econometrics > Big Data
  2. Alexandre Belloni & Victor Chernozhukov, 2011. "High Dimensional Sparse Econometric Models: An Introduction," Papers 1106.5242, arXiv.org, revised Sep 2011.

    Mentioned in:

    1. > Econometrics > Big Data

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.

    Mentioned in:

    1. Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain (ECTA 2012) in ReplicationWiki ()
  2. Alexandre Belloni & Victor Chernozhukov & Christian Hansen & Damian Kozbur, 2016. "Inference in High-Dimensional Panel Models With an Application to Gun Control," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 590-605, October.

    Mentioned in:

    1. Inference in High Dimensional Panel Models with an Application to Gun Control (J Business & Econ Statistics 2016) in ReplicationWiki ()

Working papers

  1. Philipp Bach & Oliver Schacht & Victor Chernozhukov & Sven Klaassen & Martin Spindler, 2024. "Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study," Papers 2402.04674, arXiv.org.

    Cited by:

    1. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
    2. Ballinari, Daniele, 2024. "Calibrating doubly-robust estimators with unbalanced treatment assignment," Economics Letters, Elsevier, vol. 241(C).

  2. Victor Chernozhukov & Christian Hansen & Nathan Kallus & Martin Spindler & Vasilis Syrgkanis, 2024. "Applied Causal Inference Powered by ML and AI," Papers 2403.02467, arXiv.org.

    Cited by:

    1. Ravi B. Sojitra & Vasilis Syrgkanis, 2024. "Dynamic Local Average Treatment Effects," Papers 2405.01463, arXiv.org, revised May 2024.
    2. Martin Huber, 2024. "An introduction to causal discovery," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 160(1), pages 1-16, December.
    3. Hiroyuki Egami & Md. Shafiur Rahman & Tsuyoshi Yamamoto & Chihiro Egami & Takahisa Wakabayashi, 2024. "Causal effect of video gaming on mental well-being in Japan 2020–2022," Nature Human Behaviour, Nature, vol. 8(10), pages 1943-1956, October.
    4. Aur'elien Bibaut & Nathan Kallus, 2024. "Demistifying Inference after Adaptive Experiments," Papers 2405.01281, arXiv.org.
    5. Martin Huber, 2024. "An Introduction to Causal Discovery," Papers 2407.08602, arXiv.org.
    6. Masahiro Kato, 2024. "Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects," Papers 2403.03240, arXiv.org.

  3. Victor Chernozhukov & Han Hong, 2023. "An MCMC Approach to Classical Estimation," Papers 2301.07782, arXiv.org.

    Cited by:

    1. Chatterjee Pratiti, 2019. "Asymmetric impact of uncertainty in recessions: are emerging countries more vulnerable?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(2), pages 1-27, April.
    2. Roberta Serafini & J. Bruha & B. Pierluigi, 2011. "Euro area labour markets: different reaction to shocks?," EcoMod2011 2970, EcoMod.
    3. Lucia Del Carpio & Samuel Kapon & Sylvain Chassang, 2022. "Using Divide-and-Conquer to Improve Tax Collection: Evidence from the Field," Working Papers 301, Princeton University, Department of Economics, Center for Economic Policy Studies..
    4. Patrick Bajari & Jeremy T. Fox & Stephen Ryan, 2006. "Evaluating Wireless Carrier Consolidation Using Semiparametric Demand Estimation," NBER Working Papers 12425, National Bureau of Economic Research, Inc.
    5. Yuriy Gorodnichenko & Anna Mikusheva & Serena Ng, 2011. "Estimators for Persistent and Possibly Non-Stationary Data with Classical Properties," NBER Working Papers 17424, National Bureau of Economic Research, Inc.
    6. Li, Wenli & Meghir, Costas & Oswald, Florian, 2022. "Consumer Bankruptcy, Mortgage Default and Labor Supply," CEPR Discussion Papers 17117, C.E.P.R. Discussion Papers.
    7. Mike G. Tsionas, 2023. "Linex and double-linex regression for parameter estimation and forecasting," Annals of Operations Research, Springer, vol. 323(1), pages 229-245, April.
    8. Jun, Sung Jae & Pinkse, Joris & Wan, Yuanyuan, 2011. "-Consistent robust integration-based estimation," Journal of Multivariate Analysis, Elsevier, vol. 102(4), pages 828-846, April.
    9. Xiaohong Chen & Timothy M. Christensen & Elie Tamer, 2017. "Monte Carlo confidence sets for identified sets," CeMMAP working papers CWP43/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Haroon Mumtaz & Paolo Surico, 2015. "The Transmission Mechanism In Good And Bad Times," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(4), pages 1237-1260, November.
    11. Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2017. "Smoothed GMM for quantile models," Papers 1707.03436, arXiv.org, revised Feb 2018.
    12. Erik Figueiredo & Luiz Renato Lima & Gianluca Orefice, 2016. "Migration and Regional Trade Agreements: A (New) Gravity Estimation," Review of International Economics, Wiley Blackwell, vol. 24(1), pages 99-125, February.
    13. Greg Kaplan, 2007. "Inequality and the Lifecycle," 2007 Meeting Papers 262, Society for Economic Dynamics.
    14. Kaplan, David M. & Sun, Yixiao, 2012. "Smoothed Estimating Equations For Instrumental Variables Quantile Regression," University of California at San Diego, Economics Working Paper Series qt888657tp, Department of Economics, UC San Diego.
    15. Andreas Tryphonides, 2023. "Online Appendix to "Identifying Preferences when Households are Financially Constrained"," Online Appendices 21-242, Review of Economic Dynamics.
    16. Xiaohong Chen & Timothy M. Christensen & Keith O'Hara & Elie Tamer, 2016. "MCMC confidence sets for identified sets," CeMMAP working papers CWP28/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    17. Ando, Tomohiro & Bai, Jushan & Li, Kunpeng, 2022. "Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 230(1), pages 20-38.
    18. Gyuhyeong Goh & Jisang Yu, 2022. "Causal inference with some invalid instrumental variables: A quasi‐Bayesian approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(6), pages 1432-1451, December.
    19. Bournakis, Ioannis & Tsionas, Mike G., 2023. "A Non-Parametric Estimation of Productivity with Idiosyncratic and Aggregate Shocks: The Role of Research and Development (R&D) and Corporate Tax," MPRA Paper 118100, University Library of Munich, Germany.
    20. David T. Frazier & Gael M. Martin & Ruben Loaiza-Maya, 2022. "Variational Bayes in State Space Models: Inferential and Predictive Accuracy," Monash Econometrics and Business Statistics Working Papers 1/22, Monash University, Department of Econometrics and Business Statistics.
    21. Bonciani, Dario, 2014. "Uncertainty shocks: it's a matter of habit," MPRA Paper 59370, University Library of Munich, Germany.
    22. Guerron-Quintana, Pablo & Inoue, Atsushi & Kilian, Lutz, 2017. "Impulse response matching estimators for DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 144-155.
    23. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2009. "Finite sample inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 152(2), pages 93-103, October.
    24. Le-Yu Chen & Sokbae (Simon) Lee, 2017. "Exact computation of GMM estimators for instrumental variable quantile regression models," CeMMAP working papers CWP52/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    25. Yuan Liao & Anna Simoni, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," Papers 1212.3267, arXiv.org, revised Nov 2013.
    26. Alessandro Casini & Pierre Perron, 2018. "Generalized Laplace Inference in Multiple Change-Points Models," Papers 1803.10871, arXiv.org, revised Jan 2021.
    27. Canova, Fabio & Matthes, Christian, 2018. "A composite likelihood approach for dynamic structural models," CEPR Discussion Papers 13245, C.E.P.R. Discussion Papers.
    28. Harrison Fell & Shanjun Li & Anthony Paul, 2012. "A New Look at Residential Electricity Demand Using Household Expenditure Data," Working Papers 2012-04, Colorado School of Mines, Division of Economics and Business.
    29. Grigory Franguridi & Bulat Gafarov & Kaspar Wuthrich, 2020. "Bias correction for quantile regression estimators," Papers 2011.03073, arXiv.org, revised Nov 2024.
    30. Takuo Matsubara & Jeremias Knoblauch & François‐Xavier Briol & Chris J. Oates, 2022. "Robust generalised Bayesian inference for intractable likelihoods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 997-1022, July.
    31. Francisco Peñaranda & Jón Daníelsson, 2007. "On the impact of fundamentals, liquidity and coordination on market stability," Economics Working Papers 1003, Department of Economics and Business, Universitat Pompeu Fabra, revised Mar 2010.
    32. Stephanie Schmitt‐Grohé & Martín Uribe, 2012. "What's News in Business Cycles," Econometrica, Econometric Society, vol. 80(6), pages 2733-2764, November.
    33. Komunjer, Ivana, 2005. "Quasi-maximum likelihood estimation for conditional quantiles," Journal of Econometrics, Elsevier, vol. 128(1), pages 137-164, September.
    34. Igari Ryosuke & Takahiro Hoshino, 2017. "Semiparametric Quasi-Bayesian Inference with Dirichlet Process Priors: Application to Nonignorable Missing Responses," Keio-IES Discussion Paper Series 2017-020, Institute for Economics Studies, Keio University.
    35. Jeremy Lise & Costas Meghir & Jean-Marc Robin, 2013. "Mismatch, sorting and wage dynamics," IFS Working Papers W13/16, Institute for Fiscal Studies.
    36. Popp, Aaron & Zhang, Fang, 2016. "The macroeconomic effects of uncertainty shocks: The role of the financial channel," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 319-349.
    37. Fusejima, Koki, 2024. "Identification of multi-valued treatment effects with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 238(1).
    38. Ana M. Fernandes & Peter J. Klenow & Sergii Meleshchuk & Denisse Pierola & Andrés Rodríguez-Clare, 2018. "The Intensive Margin in Trade," NBER Working Papers 25195, National Bureau of Economic Research, Inc.
    39. Yasufumi Gemma & Takushi Kurozumi & Mototsugu Shintani, 2017. "Trend Inflation and Evolving Inflation Dynamics: A Bayesian GMM Analysis of the Generalized New Keynesian Phillips Curve," IMES Discussion Paper Series 17-E-10, Institute for Monetary and Economic Studies, Bank of Japan.
    40. Noriko Amano, 2018. "Nutrition Inequality: The Role of Prices, Income, and Preferences," 2018 Meeting Papers 453, Society for Economic Dynamics.
    41. Ryosuke Igari & Takahiro Hoshino, 2018. "A Bayesian Gamma Frailty Model Using the Sum of Independent Random Variables: Application of the Estimation of an Interpurchase Timing Model," Keio-IES Discussion Paper Series 2018-021, Institute for Economics Studies, Keio University.
    42. Ruge-Murcia, Francisco, 2012. "Estimating nonlinear DSGE models by the simulated method of moments: With an application to business cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 36(6), pages 914-938.
    43. Manuel Arellano & Stéphane Bonhomme, 2007. "Robust priors in nonlinear panel data models," CeMMAP working papers CWP07/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    44. Jean-Pierre Florens & Anna Simoni, 2021. "Revisiting identification concepts in Bayesian analysis," Papers 2110.09954, arXiv.org.
    45. Han Hong & Huiyu Li & Jessie Li, 2019. "BLP Estimation Using Laplace Transformation and Overlapping Simulation Draws," Working Paper Series 2019-24, Federal Reserve Bank of San Francisco.
    46. Chung, Ray S.W. & So, Mike K.P. & Chu, Amanda M.Y. & Chan, Thomas W.C., 2020. "Regularization of Bayesian quasi-likelihoods constructed from complex estimating functions," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    47. Sebastian Heise & Justin R. Pierce & Georg Schaur & Peter K. Schott, 2024. "Tariff Rate Uncertainty and the Structure of Supply Chains," International Finance Discussion Papers 1389, Board of Governors of the Federal Reserve System (U.S.).
    48. Vittorio Bassi & Raffaela Muoio & Tommaso Porzio & Ritwika Sen & Esau Tugume, 2022. "Achieving Scale Collectively," Econometrica, Econometric Society, vol. 90(6), pages 2937-2978, November.
    49. Giovanni Caggiano & Efrem Castelnuovo & Valentina Colombo & Gabriela Nodari, 2014. "Estimating Fiscal Multipliers: News from a Nonlinear World," Melbourne Institute Working Paper Series wp2014n26, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    50. Manuel Arellano & Stéphane Bonhomme, 2017. "Quantile Selection Models With an Application to Understanding Changes in Wage Inequality," Econometrica, Econometric Society, vol. 85, pages 1-28, January.
    51. Fotiou, Alexandra & Shen, Wenyi & Yang, Shu-Chun S., 2020. "The fiscal state-dependent effects of capital income tax cuts," Journal of Economic Dynamics and Control, Elsevier, vol. 117(C).
    52. Jeremy Lise & Costas Meghir & Jean-Marc Robin, 2016. "Matching, Sorting and Wages," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 19, pages 63-87, January.
    53. Miguel Almunia & Benjamin Lockwood & Kimberley Ann Scharf, 2017. "More Giving or More Givers? The Effects of Tax Incentives on Charitable Donations in the UK," CESifo Working Paper Series 6591, CESifo.
    54. Jean-Jacques Forneron & Liang Zhong, 2023. "Convexity Not Required: Estimation of Smooth Moment Condition Models," Papers 2304.14386, arXiv.org.
    55. David Powell, 2013. "A New Framework for Estimation of Quantile Treatment Effects Nonseparable Disturbance in the Presence of Covariates," Working Papers WR-824-1, RAND Corporation.
    56. Andrew Chia, 2021. "Automatically Differentiable Random Coefficient Logistic Demand Estimation," Papers 2106.04636, arXiv.org.
    57. Alhamzawi, Rahim & Yu, Keming, 2013. "Conjugate priors and variable selection for Bayesian quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 209-219.
    58. Ana M. Fernandes & Peter J. Klenow & Sergii Meleshchuk & Martha Denisse Pierola & Andrés Rodríguez- Clare, 2023. "The Intensive Margin in Trade: How Big and How Important?," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(3), pages 320-354, July.
    59. Jarkko Jääskelä & Kristoffer Nimark, 2008. "A Medium-scale Open Economy Model of Australia," RBA Research Discussion Papers rdp2008-07, Reserve Bank of Australia.
    60. Huigang Chen & Mr. Alin T Mirestean & Mr. Charalambos G Tsangarides, 2011. "Limited Information Bayesian Model Averaging for Dynamic Panels with An Application to a Trade Gravity Model," IMF Working Papers 2011/230, International Monetary Fund.
    61. Marcelo Fernandes & Emmanuel Guerre & Eduardo Horta, 2021. "Smoothing Quantile Regressions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 338-357, January.
    62. Jiti Gao & Han Hong, 2014. "Nonparametric Regression Approach to Bayesian Estimation," Monash Econometrics and Business Statistics Working Papers 25/14, Monash University, Department of Econometrics and Business Statistics.
    63. Petrova, Katerina, 2019. "A quasi-Bayesian local likelihood approach to time varying parameter VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 286-306.
    64. Alan J. Auerbach & Yuriy Gorodnichenko, 2010. "Measuring the Output Responses to Fiscal Policy," NBER Chapters, in: Fiscal Policy (Trans-Atlantic Public Economics Seminar, TAPES), pages 1-27, National Bureau of Economic Research, Inc.
    65. Otero, Karina V., 2016. "Intensity of default in sovereign bonds: Estimation of an unobservable process," MPRA Paper 86782, University Library of Munich, Germany.
    66. Ron Gallant & Raffaella Giacomini & Giuseppe Ragusa, 2013. "Generalized method of moments with latent variables," CeMMAP working papers CWP50/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    67. Simon Loertscher & Andras Niedermayer, 2012. "Assessing the Performance of Simple Contracts Empirically:The Case of Percentage Fees," Department of Economics - Working Papers Series 1163, The University of Melbourne.
    68. Abby Alpert & David Powell, 2014. "Estimating Intensive and Extensive Tax Responsiveness Do Older Workers Respond to Income Taxes?," Working Papers WR-987-1, RAND Corporation.
    69. Igari, Ryosuke & Hoshino, Takahiro, 2018. "A Bayesian data combination approach for repeated durations under unobserved missing indicators: Application to interpurchase-timing in marketing," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 150-166.
    70. Stephanie Schmitt-Grohé & Martín Uribe, 2010. "Business Cycles With A Common Trend in Neutral and Investment-Specific Productivity," NBER Working Papers 16071, National Bureau of Economic Research, Inc.
    71. Hlouskova, Jaroslava & Sögner, Leopold, 2015. "GMM Estimation of Affine Term Structure Models," Economics Series 315, Institute for Advanced Studies.
    72. Mi Luo & Simon Mongey, 2019. "Assets and Job Choice: Student Debt, Wages, and Job Satisfaction," 2019 Meeting Papers 1220, Society for Economic Dynamics.
    73. Leon Zolotoy & Don O’Sullivan & Geoffrey P. Martin & Robert M. Wiseman, 2021. "Stakeholder Agency Relationships: CEO Stock Options and Corporate Tax Avoidance," Journal of Management Studies, Wiley Blackwell, vol. 58(3), pages 782-814, May.
    74. Jean-Jacques Forneron, 2019. "Detecting Identification Failure in Moment Condition Models," Papers 1907.13093, arXiv.org, revised Oct 2023.
    75. Benjamin Friedrich & Lisa Laun & Costas Meghir & Luigi Pistaferri, 2021. "Earnings dynamics and firm-level shocks," IFS Working Papers W21/33, Institute for Fiscal Studies.
    76. Haroon Mumtaz & Gabor Pinter & Konstantinos Theodoridis, 2018. "What Do Vars Tell Us About The Impact Of A Credit Supply Shock?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(2), pages 625-646, May.
    77. In Kim & In-Seok Baek & Jaesun Noh & Sol Kim, 2007. "The role of stochastic volatility and return jumps: reproducing volatility and higher moments in the KOSPI 200 returns dynamics," Review of Quantitative Finance and Accounting, Springer, vol. 29(1), pages 69-110, July.
    78. Fabio Canova & Christian Matthes, 2021. "Dealing with misspecification in structural macroeconometric models," Quantitative Economics, Econometric Society, vol. 12(2), pages 313-350, May.
    79. Veyssiere, Luc Pierre, 2009. "A three essays dissertation on agricultural and environmental microeconomics," ISU General Staff Papers 200901010800001958, Iowa State University, Department of Economics.
    80. Benoit, Dries F. & Van den Poel, Dirk, 2017. "bayesQR: A Bayesian Approach to Quantile Regression," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i07).
    81. Anna Kormilitsina, 2009. "Oil Price Shocks and the Optimality of Monetary Policy," Departmental Working Papers 0901, Southern Methodist University, Department of Economics.
    82. Florian Oswald, 2015. "Regional Shocks, Migration and Homeownership," 2015 Meeting Papers 759, Society for Economic Dynamics.
    83. Rubio-Ramírez, Juan Francisco & Schorfheide, Frank & Fernández-Villaverde, Jesús, 2015. "Solution and Estimation Methods for DSGE Models," CEPR Discussion Papers 11032, C.E.P.R. Discussion Papers.
    84. Haroon Mumtaz & Roman Sustek, 2023. "Global house prices since 1950," Discussion Papers 2307, Centre for Macroeconomics (CFM).
    85. Paul A. Borochin, 2014. "When Does a Merger Create Value? Using Option Prices to Elicit Market Beliefs," Financial Management, Financial Management Association International, vol. 43(2), pages 445-466, June.
    86. Victor Chernozhukov & Christian Hansen, 2013. "Quantile models with endogeneity," CeMMAP working papers CWP25/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    87. Giuseppe Ragusa, 2007. "Bayesian Likelihoods for Moment Condition Models," Working Papers 060714, University of California-Irvine, Department of Economics.
    88. Giovanni Caggiano & Efrem Castelnuovo & Gabriela Nodari, 2020. "Uncertainty and monetary policy in good and bad times: A Replication of the VAR investigation by Bloom (2009)," CAMA Working Papers 2020-74, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    89. Porzio, Tommaso & Heise, Sebastian, 2019. "Spatial Wage Gaps in Frictional Labor Markets," CEPR Discussion Papers 14197, C.E.P.R. Discussion Papers.
    90. Alexandre Belloni & Victor Chernozhukov, 2009. "Posterior Inference in Curved Exponential Families under Increasing Dimensions," Papers 0904.3132, arXiv.org, revised Apr 2014.
    91. George Monokroussos, 2006. "Dynamic Limited Dependent Variable Modeling and U.S. Monetary Policy," Discussion Papers 06-02, University at Albany, SUNY, Department of Economics.
    92. Chen Jau-er, 2015. "Factor instrumental variable quantile regression," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(1), pages 71-92, February.
    93. Christiano, Lawrence J. & Trabandt, Mathias & Walentin, Karl, 2011. "Introducing financial frictions and unemployment into a small open economy model," Journal of Economic Dynamics and Control, Elsevier, vol. 35(12), pages 1999-2041.
    94. Olivier Coibion & Yuriy Gorodnichenko, 2011. "Strategic Interaction among Heterogeneous Price-Setters in an Estimated DSGE Model," The Review of Economics and Statistics, MIT Press, vol. 93(3), pages 920-940, August.
    95. Gregor Jarosch, 2014. "Falling off the Ladder - Earnings Losses from Job Loss," 2014 Meeting Papers 1248, Society for Economic Dynamics.
    96. Jean-Pierre Florens & Anna Simoni, 2021. "Gaussian Processes and Bayesian Moment Estimation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 482-492, March.
    97. Parente, Paulo M.D.C. & Smith, Richard J., 2011. "Gel Methods For Nonsmooth Moment Indicators," Econometric Theory, Cambridge University Press, vol. 27(1), pages 74-113, February.
    98. Xu, Ke-Li, 2020. "Inference of local regression in the presence of nuisance parameters," Journal of Econometrics, Elsevier, vol. 218(2), pages 532-560.
    99. Olli Saarela & Elja Arjas, 2015. "Non-parametric Bayesian Hazard Regression for Chronic Disease Risk Assessment," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 609-626, June.
    100. Boese-Schlosser, Vanessa A. & Eberhardt, Markus, 2024. "Democracy Doesn’t Always Happen Over Night: Regime Change in Stages and Economic Growth," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue Forthcomi, pages 1-29.
    101. Jesús Fernández-Villaverde, 2010. "The econometrics of DSGE models," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 1(1), pages 3-49, March.
    102. Goodhead, Robert, 2024. "The economic impact of yield curve compression: Evidence from euro area forward guidance and unconventional monetary policy," European Economic Review, Elsevier, vol. 164(C).
    103. Bruins, Marianne & Duffy, James A. & Keane, Michael P. & Smith, Anthony A., 2018. "Generalized indirect inference for discrete choice models," Journal of Econometrics, Elsevier, vol. 205(1), pages 177-203.
    104. Anna Kormilitsina & Denis Nekipelov, 2015. "Consistent Variance of the Laplace Type Estimators: Application to DSGE Models," Departmental Working Papers 1510, Southern Methodist University, Department of Economics.
    105. Jian, Zhihong & Li, Xupei & Zhu, Zhican, 2020. "Sequential forecasting of downside extreme risk during overnight and daytime: Evidence from the Chinese Stock Market☆," Pacific-Basin Finance Journal, Elsevier, vol. 64(C).
    106. Fontaine, Idriss & Razafindravaosolonirina, Justinien & Didier, Laurent, 2018. "Chinese policy uncertainty shocks and the world macroeconomy: Evidence from STVAR," China Economic Review, Elsevier, vol. 51(C), pages 1-19.
    107. Grigory Franguridi & Bulat Gafarov & Kaspar Wüthrich, 2021. "Conditional Quantile Estimators: A Small Sample Theory," CESifo Working Paper Series 9046, CESifo.
    108. Jiang, Wenxin, 2017. "On limiting distribution of quasi-posteriors under partial identification," Econometrics and Statistics, Elsevier, vol. 3(C), pages 60-72.
    109. Tingting Cheng & Jiti Gao & Peter CB Phillips, 2017. "Bayesian estimation based on summary statistics: Double asymptotics and practice," Monash Econometrics and Business Statistics Working Papers 4/17, Monash University, Department of Econometrics and Business Statistics.
    110. Chunrong Ai & Xiaohong Chen, 2009. "Semiparametric efficiency bound for models of sequential moment restrictions containing unknown functions," CeMMAP working papers CWP28/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    111. Ran Gu, 2023. "Human Capital and the Business Cycle Effects on the Postgraduate Wage Premium," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 48, pages 345-376, April.
    112. Takushi Kurozumi & Ryohei Oishi & Willem Van Zandweghe, 2022. "Sticky Information Versus Sticky Prices Revisited: A Bayesian VAR-GMM Approach," Working Papers 22-34, Federal Reserve Bank of Cleveland.
    113. Thomai Filippeli & Konstantinos Theodoridis, 2014. "DSGE Priors for BVAR Models," Working Papers 713, Queen Mary University of London, School of Economics and Finance.
    114. Guner, Nezih & Ruggieri, Alessandro, 2022. "Misallocation and Inequality," CEPR Discussion Papers 17113, C.E.P.R. Discussion Papers.
    115. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    116. Wu Wang & Zhongyi Zhu, 2017. "Conditional empirical likelihood for quantile regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(1), pages 1-16, January.
    117. Timothy Christensen & Hyungsik Roger Moon & Frank Schorfheide, 2022. "Optimal Decision Rules when Payoffs are Partially Identified," Papers 2204.11748, arXiv.org, revised May 2023.
    118. Sebastian Heise, 2018. "Firm-to-Firm Relationships and Price Rigidity: Theory and Evidence," 2018 Meeting Papers 937, Society for Economic Dynamics.
    119. Yinchu Zhu, 2018. "Learning non-smooth models: instrumental variable quantile regressions and related problems," Papers 1805.06855, arXiv.org, revised Sep 2019.
    120. Wenli Li & Costas Meghir & Florian Oswald, 2022. "Consumer Bankrupcty, Mortgage Default and Labor Supply," Working Papers hal-03882830, HAL.
    121. Hamish Low & Costas Meghir, 2017. "The Use of Structural Models in Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 33-58, Spring.
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  4. Victor Chernozhukov & Ivan Fernandez-Val & Siyi Luo, 2023. "Distribution regression with sample selection and UK wage decomposition," CeMMAP working papers 09/23, Institute for Fiscal Studies.

    Cited by:

    1. Onil Boussim, 2023. "Correcting Selection Bias in Standardized Test Scores Comparisons," Papers 2309.10642, arXiv.org, revised Jun 2024.

  5. Patrick Bajari & Zhihao Cen & Victor Chernozhukov & Manoj Manukonda & Suhas Vijaykumar & Jin Wang & Ramon Huerta & Junbo Li & Ling Leng & George Monokroussos & Shan Wan, 2023. "Hedonic Prices and Quality Adjusted Price Indices Powered by AI," Papers 2305.00044, arXiv.org.

    Cited by:

    1. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2024. "Model Averaging and Double Machine Learning," Papers 2401.01645, arXiv.org, revised Sep 2024.

  6. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2022. "Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals," Papers 2203.13887, arXiv.org, revised Jun 2023.

    Cited by:

    1. Victor Quintas-Martinez & Mohammad Taha Bahadori & Eduardo Santiago & Jeff Mu & Dominik Janzing & David Heckerman, 2024. "Multiply-Robust Causal Change Attribution," Papers 2404.08839, arXiv.org, revised Sep 2024.

  7. Guillaume Carlier & Victor Chernozhukov & Gwendoline de Bie & Alfred Galichon, 2022. "Correction to: Vector Quantile Regression and Optimal Transport, from Theory to Numerics," Post-Print hal-03896159, HAL.

    Cited by:

    1. Bernd Fitzenberger & Roger Koenker & José Machado & Blaise Melly, 2022. "Economic applications of quantile regression 2.0," Empirical Economics, Springer, vol. 62(1), pages 1-6, January.
    2. Linjie Wang & Jean‐Paul Chavas & Jian Li, 2024. "Dynamic linkages in agricultural and energy markets: A quantile impulse response approach," Agricultural Economics, International Association of Agricultural Economists, vol. 55(4), pages 639-676, July.

  8. Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2021. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," Papers 2112.13398, arXiv.org, revised May 2024.

    Cited by:

    1. Vafa, Keyon & Athey, Susan & Blei, David M., 2024. "Estimating Wage Disparities Using Foundation Models," Research Papers 4206, Stanford University, Graduate School of Business.
    2. Hünermund Paul & Louw Beyers & Caspi Itamar, 2023. "Double machine learning and automated confounder selection: A cautionary tale," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-12, January.
    3. Geonwoo Kim & Suyong Song, 2024. "Double/Debiased CoCoLASSO of Treatment Effects with Mismeasured High-Dimensional Control Variables," Papers 2408.14671, arXiv.org.

  9. Victor Chernozhukov & Christian Hansen & Yuan Liao & Yinchu Zhu, 2021. "Inference for Low-Rank Models," Papers 2107.02602, arXiv.org, revised Jan 2023.

    Cited by:

    1. Choi, Jungjun & Kwon, Hyukjun & Liao, Yuan, 2024. "Inference for low-rank completion without sample splitting with application to treatment effect estimation," Journal of Econometrics, Elsevier, vol. 240(1).
    2. Hugo Freeman & Martin Weidner, 2021. "Linear panel regressions with two-way unobserved heterogeneity," CeMMAP working papers CWP39/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  10. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2021. "A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees," Papers 2105.15197, arXiv.org, revised Oct 2022.

    Cited by:

    1. Dmitry Arkhangelsky & Kazuharu Yanagimoto & Tom Zohar, 2024. "Flexible Analysis of Individual Heterogeneity in Event Studies: Application to the Child Penalty," Papers 2403.19563, arXiv.org.
    2. Jikai Jin & Vasilis Syrgkanis, 2024. "Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation," Papers 2402.14264, arXiv.org, revised Mar 2024.
    3. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2023. "Source Condition Double Robust Inference on Functionals of Inverse Problems," Papers 2307.13793, arXiv.org.
    4. Rahul Singh & Liyuan Xu & Arthur Gretton, 2021. "Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves," Papers 2111.03950, arXiv.org, revised Jul 2023.
    5. Rahul Singh, 2021. "Generalized Kernel Ridge Regression for Causal Inference with Missing-at-Random Sample Selection," Papers 2111.05277, arXiv.org.
    6. David Bruns-Smith & Oliver Dukes & Avi Feller & Elizabeth L. Ogburn, 2023. "Augmented balancing weights as linear regression," Papers 2304.14545, arXiv.org, revised Jun 2024.
    7. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression: Long Term, Mediated, and Time Varying Treatment Effects," Papers 2112.14249, arXiv.org, revised Mar 2024.

  11. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python," Papers 2104.03220, arXiv.org, revised Dec 2021.

    Cited by:

    1. Chakravorty, Bhaskar & Arulampalam, Wiji & Bhatiya, Apurav Yash & Imbert, Clement & Rathelot, Roland, 2021. "Can information about jobs improve the effectiveness of vocational training? Experimental evidence from India," CAGE Online Working Paper Series 567, Competitive Advantage in the Global Economy (CAGE).
    2. Waverly Wei & Maya Petersen & Mark J van der Laan & Zeyu Zheng & Chong Wu & Jingshen Wang, 2023. "Efficient targeted learning of heterogeneous treatment effects for multiple subgroups," Biometrics, The International Biometric Society, vol. 79(3), pages 1934-1946, September.
    3. Philipp Schwarz & Oliver Schacht & Sven Klaassen & Daniel Grunbaum & Sebastian Imhof & Martin Spindler, 2024. "Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework?," Papers 2406.11308, arXiv.org.
    4. Tommaso Manfè & Luca Nunziata, 2023. "Difference-In-Difference Design With Repeated Cross-Sections Under Compositional Changes: a Monte-Carlo Evaluation of Alternative Approaches," "Marco Fanno" Working Papers 0305, Dipartimento di Scienze Economiche "Marco Fanno".

  12. Victor Chernozhukov & Hiroyuki Kasahara & Paul Schrimpf, 2021. "The Association of Opening K-12 Schools with the Spread of COVID-19 in the United States: County-Level Panel Data Analysis," Papers 2102.10453, arXiv.org, revised Jun 2021.

    Cited by:

    1. Kurmann, Andre & Lalé, Etienne, 2022. "School Closures and Effective In-Person Learning during COVID-19: When, Where, and for Whom," IZA Discussion Papers 14984, Institute of Labor Economics (IZA).
    2. Ingo E. Isphording & Marc Diederichs & Reyn van Ewijk & Nico Pestel, 2021. "Schools under mandatory testing can mitigate the spread of SARS-CoV-2," Working Papers 2116, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    3. Raouf Boucekkine & Shankha Chakraborty & Aditya Goenka & Lin Liu, 2024. "Economic epidemiological modelling: A progress report," Post-Print hal-04615577, HAL.
    4. Emanuele Amodio & Michele Battisti & Antonio Francesco Gravina & Andrea Mario Lavezzi & Giuseppe Maggio, 2023. "School‐age vaccination, school openings and Covid‐19 diffusion," Health Economics, John Wiley & Sons, Ltd., vol. 32(5), pages 1084-1100, May.
    5. Dan Goldhaber & Scott A. Imberman & Katharine O. Strunk & Bryant Hopkins & Nate Brown & Erica Harbatkin & Tara Kilbride, 2021. "To What Extent Does In-Person Schooling Contribute to the Spread of COVID-19? Evidence from Michigan and Washington," NBER Working Papers 28455, National Bureau of Economic Research, Inc.
    6. Kisho Hoshi & Hiroyuki Kasahara & Ryo Makioka & Michio Suzuki & Satoshi Tanaka, 2021. "Trade-off between job losses and the spread of COVID-19 in Japan," The Japanese Economic Review, Springer, vol. 72(4), pages 683-716, October.
    7. Koppa, Vijetha & West, Jeremy, 2021. "School Reopenings, COVID-19, and Employment," Santa Cruz Department of Economics, Working Paper Series qt07w4z6vb, Department of Economics, UC Santa Cruz.
    8. Lattanzio, Salvatore, 2024. "Schools and the transmission of Sars-Cov-2: Evidence from Italy," Economics & Human Biology, Elsevier, vol. 52(C).
    9. Amodio, Emanuele & Battisti, Michele & Kourtellos, Andros & Maggio, Giuseppe & Maida, Carmelo Massimo, 2022. "Schools opening and Covid-19 diffusion: Evidence from geolocalized microdata," European Economic Review, Elsevier, vol. 143(C).

  13. Guillaume Carlier & Victor Chernozhukov & Gwendoline De Bie & Alfred Galichon, 2021. "Vector quantile regression and optimal transport, from theory to numerics," Papers 2102.12809, arXiv.org.

    Cited by:

    1. Bernd Fitzenberger & Roger Koenker & José Machado & Blaise Melly, 2022. "Economic applications of quantile regression 2.0," Empirical Economics, Springer, vol. 62(1), pages 1-6, January.
    2. Linjie Wang & Jean‐Paul Chavas & Jian Li, 2024. "Dynamic linkages in agricultural and energy markets: A quantile impulse response approach," Agricultural Economics, International Association of Agricultural Economists, vol. 55(4), pages 639-676, July.

  14. Victor Chernozhukov & Hiroyuki Kasahara & Paul Schrimpf, 2021. "The Association of Opening K-12 Schools and Colleges with the Spread of Covid-19 in the United States: County-Level Panel Data Analysis," CESifo Working Paper Series 8929, CESifo.

    Cited by:

    1. Kurmann, Andre & Lalé, Etienne, 2022. "School Closures and Effective In-Person Learning during COVID-19: When, Where, and for Whom," IZA Discussion Papers 14984, Institute of Labor Economics (IZA).
    2. Ingo E. Isphording & Marc Diederichs & Reyn van Ewijk & Nico Pestel, 2021. "Schools under mandatory testing can mitigate the spread of SARS-CoV-2," Working Papers 2116, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    3. Emanuele Amodio & Michele Battisti & Antonio Francesco Gravina & Andrea Mario Lavezzi & Giuseppe Maggio, 2023. "School‐age vaccination, school openings and Covid‐19 diffusion," Health Economics, John Wiley & Sons, Ltd., vol. 32(5), pages 1084-1100, May.
    4. Dan Goldhaber & Scott A. Imberman & Katharine O. Strunk & Bryant Hopkins & Nate Brown & Erica Harbatkin & Tara Kilbride, 2021. "To What Extent Does In-Person Schooling Contribute to the Spread of COVID-19? Evidence from Michigan and Washington," NBER Working Papers 28455, National Bureau of Economic Research, Inc.
    5. Koppa, Vijetha & West, Jeremy, 2021. "School Reopenings, COVID-19, and Employment," Santa Cruz Department of Economics, Working Paper Series qt07w4z6vb, Department of Economics, UC Santa Cruz.
    6. Lattanzio, Salvatore, 2024. "Schools and the transmission of Sars-Cov-2: Evidence from Italy," Economics & Human Biology, Elsevier, vol. 52(C).
    7. Amodio, Emanuele & Battisti, Michele & Kourtellos, Andros & Maggio, Giuseppe & Maida, Carmelo Massimo, 2022. "Schools opening and Covid-19 diffusion: Evidence from geolocalized microdata," European Economic Review, Elsevier, vol. 143(C).

  15. Victor Chernozhukov & Hiroyuki Kasahara & Paul Schrimpf, 2021. "A Response to Philippe Lemoine's Critique on our Paper "Causal Impact of Masks, Policies, Behavior on Early Covid-19 Pandemic in the U.S."," Papers 2110.06136, arXiv.org.

    Cited by:

    1. Crudu, Federico & Di Stefano, Roberta & Mellace, Giovanni & Tiezzi, Silvia, 2024. "The gray zone: How not imposing a strict lockdown at the beginning of a pandemic can cost many lives," Labour Economics, Elsevier, vol. 89(C).

  16. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.

    Cited by:

    1. Paul Goldsmith-Pinkham & Peter Hull & Michal Kolesár, 2024. "Contamination Bias in Linear Regressions," American Economic Review, American Economic Association, vol. 114(12), pages 4015-4051, December.
    2. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2022. "Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals," Papers 2203.13887, arXiv.org, revised Jun 2023.
    3. Amandeep Singh & Ye Liu & Hema Yoganarasimhan, 2023. "Choice Models and Permutation Invariance: Demand Estimation in Differentiated Products Markets," Papers 2307.07090, arXiv.org, revised Feb 2024.
    4. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.
    5. Jonas Metzger, 2022. "Adversarial Estimators," Papers 2204.10495, arXiv.org, revised Jun 2022.
    6. Victor Quintas-Martinez, 2022. "Finite-Sample Guarantees for High-Dimensional DML," Papers 2206.07386, arXiv.org.
    7. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests," Papers 2110.03031, arXiv.org, revised Jun 2022.

  17. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.

    Cited by:

    1. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
    2. Jose E. Gomez-Gonzalez & Jorge M. Uribe & Oscar M. Valencia, 2023. "Sovereign Risk and Economic Complexity: Machine Learning Insights on Causality and Prediction," IREA Working Papers 202315, University of Barcelona, Research Institute of Applied Economics, revised Nov 2023.

  18. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.

    Cited by:

    1. Javier Alejo & Antonio F Galvao & Gabriel Montes-Rojas, 2023. "A first-stage representation for instrumental variables quantile regression," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 350-377.
    2. Aluko, Olufemi Adewale & Opoku, Eric Evans Osei, 2022. "The financial development impact of financial globalization revisited: A focus on OECD countries," International Economics, Elsevier, vol. 169(C), pages 13-29.
    3. Jun Ma & Vadim Marmer & Zhengfei Yu, 2021. "Inference on Individual Treatment Effects in Nonseparable Triangular Models," Papers 2107.05559, arXiv.org, revised Feb 2023.
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    2. Regina Pleninger & Sina Streicher & Jan-Egbert Sturm, 2021. "Do COVID-19 Containment Measures Work? Evidence from Switzerland," KOF Working papers 21-494, KOF Swiss Economic Institute, ETH Zurich.
    3. Aditya Goenka & Lin Liu & Manh-Hung Nguyen, 2021. "SIR Economic Epidemiological Models with Disease Induced Mortality," Working Papers 202103, University of Liverpool, Department of Economics.
    4. Victor Aguirregabiria & Jiaying Gu & Yao Luo & Pedro Mira, 2020. "A Dynamic Structural Model of Virus Diffusion and Network Production: A First Report," Working Papers tecipa-665, University of Toronto, Department of Economics.
    5. Francesca Caselli & Mr. Francesco Grigoli & Mr. Damiano Sandri & Mr. Antonio Spilimbergo, 2020. "Mobility under the COVID-19 Pandemic: Asymmetric Effects across Gender and Age," IMF Working Papers 2020/282, International Monetary Fund.
    6. Sabina Marchetti & Alessandro Borin & Francesco Paolo Conteduca & Giuseppe Ilardi & Giorgio Guzzetta & Piero Poletti & Patrizio Pezzotti & Antonino Bella & Paola Stefanelli & Flavia Riccardo & Stefano, 2022. "An Epidemic Model for SARS-CoV-2 with Self-Adaptive Containment Measures," Questioni di Economia e Finanza (Occasional Papers) 681, Bank of Italy, Economic Research and International Relations Area.
    7. Fernández-Villaverde, Jesús & Jones, Chad, 2020. "Estimating and Simulating a SIRD Model of COVID-19 for Many Countries, States, and Cities," CEPR Discussion Papers 14711, C.E.P.R. Discussion Papers.
    8. Cem Cakmakli & Yasin Simsek, 2023. "Bridging the Covid-19 Data and the Epidemiological Model using Time-Varying Parameter SIRD Model," Papers 2301.13692, arXiv.org.
    9. Mellace, Giovanni & Crudu, Frederico & Di Stefano, Roberta & Tiezzi, Silvia, 2022. "The Gray Zone," Discussion Papers on Economics 5/2022, University of Southern Denmark, Department of Economics.
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    Cited by:

    1. Eric Alexander Sugandi, 2022. "Indonesia’s Financial Markets and Monetary Policy Dynamics Amid the COVID-19 Pandemic," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(3), pages 411-447, September.
    2. Christian Gollier, 2020. "Cost–benefit analysis of age‐specific deconfinement strategies," Post-Print hal-03156641, HAL.
    3. Djamel KIRAT & Yassine KIRAT, 2020. "An international Comparison of the Economic Impacts of the COVID-19 Pandemic," LEO Working Papers / DR LEO 2818, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    4. Kent A. Smetters, 2020. "Stay-at-home orders and second waves: a graphical exposition," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 45(2), pages 94-103, September.
    5. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2022. "The Causal Effects of Lockdown Policies on Health and Macroeconomic Outcomes," Working Papers 22-18, Federal Reserve Bank of Philadelphia.
    6. Victor Aguirregabiria & Jiaying Gu & Yao Luo & Pedro Mira, 2020. "A Dynamic Structural Model of Virus Diffusion and Network Production: A First Report," Working Papers tecipa-665, University of Toronto, Department of Economics.
    7. Shi, Wei & Qiu, Yun & Yu, Pei & Chen, Xi, 2022. "Optimal Travel Restrictions in Epidemics," IZA Discussion Papers 15290, Institute of Labor Economics (IZA).
    8. Ichino, Andrea & Favero, Carlo A. & Rustichini, Aldo, 2020. "Restarting the economy while saving lives under Covid-19," CEPR Discussion Papers 14664, C.E.P.R. Discussion Papers.
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    Cited by:

    1. Roxanne Kovacs & Maurice Dunaiski & Janne Tukiainen, 2020. "Compulsory face mask policies do not affect community mobility in Germany," Discussion Papers 136, Aboa Centre for Economics.
    2. Simon Porcher & Thomas Renault, 2021. "Social distancing beliefs and human mobility: Evidence from Twitter," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03205158, HAL.
    3. Regina Pleninger & Sina Streicher & Jan-Egbert Sturm, 2021. "Do COVID-19 Containment Measures Work? Evidence from Switzerland," KOF Working papers 21-494, KOF Swiss Economic Institute, ETH Zurich.
    4. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2022. "The Causal Effects of Lockdown Policies on Health and Macroeconomic Outcomes," Working Papers 22-18, Federal Reserve Bank of Philadelphia.
    5. Sabina Marchetti & Alessandro Borin & Francesco Paolo Conteduca & Giuseppe Ilardi & Giorgio Guzzetta & Piero Poletti & Patrizio Pezzotti & Antonino Bella & Paola Stefanelli & Flavia Riccardo & Stefano, 2022. "An Epidemic Model for SARS-CoV-2 with Self-Adaptive Containment Measures," Questioni di Economia e Finanza (Occasional Papers) 681, Bank of Italy, Economic Research and International Relations Area.
    6. Mellace, Giovanni & Crudu, Frederico & Di Stefano, Roberta & Tiezzi, Silvia, 2022. "The Gray Zone," Discussion Papers on Economics 5/2022, University of Southern Denmark, Department of Economics.
    7. Stephen C. Miller, 2020. "COVID-19 Case Growth vs. Outcomes: Comparing Regions in the United States," Journal of Private Enterprise, The Association of Private Enterprise Education, vol. 35(Winter 20), pages 29-41.
    8. Fernández-Villaverde, Jesús & Arias, Jonas & Rubio-Ramírez, Juan Francisco & Shin, Minchul, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," CEPR Discussion Papers 15951, C.E.P.R. Discussion Papers.
    9. Seres, Gyula & Balleyer, Anna & Cerutti, Nicola & Friedrichsen, Jana & Süer, Müge, 2021. "Face Mask Use and Physical Distancing Before and After Mandatory Masking: No Evidence on Risk Compensation in Public Waiting Lines," Rationality and Competition Discussion Paper Series 300, CRC TRR 190 Rationality and Competition.
    10. Mark Pingle, 2022. "Addressing threats like Covid: why we will tend to over-react and how we can do better," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 21(1), pages 9-23, June.
    11. Krista M. Milich & Natalie Fisher & Gisela Sobral, 2024. "Effective public health messaging for university students: lessons learned to increase adherence to safety guidelines during a pandemic," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-9, December.
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    13. HOSHI Kisho & KASAHARA Hiroyuki & MAKIOKA Ryo & SUZUKI Michio & TANAKA Satoshi, 2021. "The Heterogeneous Effects of COVID-19 on Labor Markets: People's Movement and Non-Pharmaceutical Interventions," Discussion papers 21045, Research Institute of Economy, Trade and Industry (RIETI).
    14. Daniel H. Cooper & Vaishali Garga & María Jose Luengo-Prado & Jenny Tang, 2020. "The Roles of Mobility and Masks in the Spread of COVID-19," Current Policy Perspectives 89224, Federal Reserve Bank of Boston.
    15. Dongwoo Kim & Young Jun Lee, 2021. "Vaccination strategies and transmission of COVID-19: evidence across advanced countries," Papers 2109.06453, arXiv.org, revised Jan 2022.
    16. Milosh, Maria & Painter, Marcus & Sonin, Konstantin & Van Dijcke, David & Wright, Austin L., 2021. "Unmasking partisanship: Polarization undermines public response to collective risk," Journal of Public Economics, Elsevier, vol. 204(C).
    17. Nicola Fuchs-Schündeln & Dirk Krueger & André Kurmann & Étienne Lalé & Alexander Ludwig & Irina Popova, 2021. "The fiscal and welfare effects of policy responses to the Covid-19 school closures," CIRANO Working Papers 2021s-40, CIRANO.
    18. Guo, Jiaqi & Wang, Qiang & Li, Rongrong, 2024. "Can official development assistance promote renewable energy in sub-Saharan Africa countries? A matter of institutional transparency of recipient countries," Energy Policy, Elsevier, vol. 186(C).
    19. Rafaela Rosário & Inês Fronteira & Maria R. O. Martins & Cláudia Augusto & Maria José Silva & Melanie Messer & Silvana Martins & Ana Duarte & Neida Ramos & Katharina Rathmann & Orkan Okan & Kevin Dada, 2022. "Infodemic Preparedness and COVID-19: Searching about Public Health and Social Measures Is Associated with Digital Health Literacy in University Students," IJERPH, MDPI, vol. 19(19), pages 1-9, September.
    20. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," Working Papers 21-18, Federal Reserve Bank of Philadelphia.
    21. Kunz, Johannes & Propper, Carol, 2022. "Is Hospital Quality Predictive of Pandemic Deaths? Evidence from US Counties," CEPR Discussion Papers 17365, C.E.P.R. Discussion Papers.
    22. Dorn, Florian & Lange, Berit & Braml, Martin & Gstrein, David & Nyirenda, John L.Z. & Vanella, Patrizio & Winter, Joachim & Fuest, Clemens & Krause, Gérard, 2023. "The challenge of estimating the direct and indirect effects of COVID-19 interventions – Toward an integrated economic and epidemiological approach," Economics & Human Biology, Elsevier, vol. 49(C).
    23. Miquel Oliu-Barton & Bary S R Pradelski & Nicolas Woloszko & Lionel Guetta-Jeanrenaud & Philippe Aghion & Patrick Artus & Arnaud Fontanet & Philippe Martin & Guntram B Wolff, 2022. "The Effect of COVID Certificates on Vaccine Uptake, Health Outcomes, and the Economy," Post-Print hal-03813557, HAL.
    24. Mr. Niels-Jakob H Hansen & Rui Mano, 2021. "Mask Mandates Save Lives," IMF Working Papers 2021/205, International Monetary Fund.
    25. Kisho Hoshi & Hiroyuki Kasahara & Ryo Makioka & Michio Suzuki & Satoshi Tanaka, 2021. "Trade-off between job losses and the spread of COVID-19 in Japan," The Japanese Economic Review, Springer, vol. 72(4), pages 683-716, October.
    26. Patrick Rehill & Nicholas Biddle, 2023. "Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability," Papers 2310.13240, arXiv.org, revised Mar 2024.
    27. Patrick Rehill & Nicholas Biddle, 2023. "Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making," Papers 2309.00805, arXiv.org.
    28. Federico A. Bugni & Ivan A. Canay & Steve McBride, 2023. "Decomposition and Interpretation of Treatment Effects in Settings with Delayed Outcomes," Papers 2302.11505, arXiv.org, revised Sep 2024.
    29. Alexander Karaivanov & Dongwoo Kim & Shih En Lu & Hitoshi Shigeoka, 2021. "COVID-19 Vaccination Mandates and Vaccine Uptake," NBER Working Papers 29563, National Bureau of Economic Research, Inc.
    30. Eitan Altman & Mandar Datar & Francesco Pellegrini & Samir Perlaza & Daniel Sadoc Menasché, 2022. "The Mask Game with Multiple Populations," Dynamic Games and Applications, Springer, vol. 12(1), pages 147-167, March.
    31. Mader, Sebastian & Rüttenauer, Tobias, 2021. "The effects of non-pharmaceutical interventions on COVID-19-related mortality: A generalized synthetic control approach across 169 countries," SocArXiv v2ef8, Center for Open Science.
    32. Corsetti, Giancarlo & Bodenstein, Martin & Guerrieri, Luca, 2020. "Social Distancing and Supply Disruptions in a Pandemic," CEPR Discussion Papers 14629, C.E.P.R. Discussion Papers.
    33. Davide Furceri & Siddharth Kothari & Longmei Zhang, 2021. "The effects of COVID‐19 containment measures on the Asia‐Pacific region," Pacific Economic Review, Wiley Blackwell, vol. 26(4), pages 469-497, October.
    34. Kim, Suji & Lee, Sujin & Ko, Eunjeong & Jang, Kitae & Yeo, Jiho, 2021. "Changes in car and bus usage amid the COVID-19 pandemic: Relationship with land use and land price," Journal of Transport Geography, Elsevier, vol. 96(C).
    35. Lindskog, Annika & Olsson, Ola, 2023. "Conditional Persistence? Historical Disease Exposure and Government Response to COVID-19," Working Papers in Economics 835, University of Gothenburg, Department of Economics, revised 11 Dec 2024.
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    39. Axel C. Mühlbacher & Andrew Sadler & Yvonne Jordan, 2022. "Population preferences for non-pharmaceutical interventions to control the SARS-CoV-2 pandemic: trade-offs among public health, individual rights, and economics," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(9), pages 1483-1496, December.
    40. Daiji Kawaguchi & Sagiri Kitao & Manabu Nose, 2022. "The impact of COVID-19 on Japanese firms: mobility and resilience via remote work," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 29(6), pages 1419-1449, December.
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    51. Shumway, Spencer & Hopper, Jonas & Tolman, Ethan Richard & Ferguson, Daniel & Hubble, Gabriella & Patterson, David & Jensen, Jamie, 2020. "Using structural equation modeling to model compliance with COVID-19 related non-pharmaceutical interventions amongst university students in the United States," SocArXiv 9nzr7, Center for Open Science.
    52. Julliard, Christian & Shi, Ran & Yuan, Kathy, 2023. "The spread of COVID-19 in London: Network effects and optimal lockdowns," Journal of Econometrics, Elsevier, vol. 235(2), pages 2125-2154.
    53. Mr. Francesco Grigoli & José M. Mota, 2020. "Assessing Targeted Containment Policies to Fight COVID-19," IMF Working Papers 2020/277, International Monetary Fund.
    54. Berta, Paolo & Bratti, Massimiliano & Fiorio, Carlo V. & Pisoni, Enrico & Verzillo, Stefano, 2021. "Administrative Border Effects in COVID-19 Related Mortality," IZA Discussion Papers 14930, Institute of Labor Economics (IZA).
    55. Andrew Atkeson & Karen A. Kopecky & Tao Zha, 2020. "Four Stylized Facts about COVID-19," FRB Atlanta Working Paper 2020-15, Federal Reserve Bank of Atlanta.
    56. Feng Wang & Xing Ge & Danwen Huang, 2022. "Government Intervention, Human Mobility, and COVID-19: A Causal Pathway Analysis from 121 Countries," Sustainability, MDPI, vol. 14(6), pages 1-26, March.
    57. Cronin, Christopher J. & Evans, William N., 2021. "Total shutdowns, targeted restrictions, or individual responsibility: How to promote social distancing in the COVID-19 Era?," Journal of Health Economics, Elsevier, vol. 79(C).
    58. Dong, Bing & Liu, Yapan & Fontenot, Hannah & Ouf, Mohamed & Osman, Mohamed & Chong, Adrian & Qin, Shuxu & Salim, Flora & Xue, Hao & Yan, Da & Jin, Yuan & Han, Mengjie & Zhang, Xingxing & Azar, Elie & , 2021. "Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review," Applied Energy, Elsevier, vol. 293(C).
    59. Callaway, Brantly & Li, Tong, 2023. "Policy evaluation during a pandemic," Journal of Econometrics, Elsevier, vol. 236(1).
    60. Karaivanov, Alexander & Lu, Shih En & Shigeoka, Hitoshi & Chen, Cong & Pamplona, Stephanie, 2021. "Face masks, public policies and slowing the spread of COVID-19: Evidence from Canada," Journal of Health Economics, Elsevier, vol. 78(C).
    61. Masuhara, Hiroaki & Hosoya, Kei, 2022. "Convergent movement of COVID-19 outbreak in Japan based on SIR model," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 29-43.
    62. Levelu, Anthonin & Sandkamp, Alexander-Nikolai, 2022. "A lockdown a day keeps the doctor away: The effectiveness of non-pharmaceutical interventions during the Covid-19 pandemic," Kiel Working Papers 2221, Kiel Institute for the World Economy (IfW Kiel).
    63. Austin L. Wright & Geet Chawla & Luke Chen & Anthony Farmer, 2020. "Tracking Mask Mandates during the COVID-19 Pandemic," Working Papers 2020-104, Becker Friedman Institute for Research In Economics.
    64. Berta, Paolo & Lovaglio, Pietro Giorgio & Paruolo, Paolo & Verzillo, Stefano, 2020. "Real Time Forecasting of Covid-19 Intensive Care Units demand," JRC Working Papers in Economics and Finance 2020-08, Joint Research Centre, European Commission.
    65. Hartl, Tobias, 2021. "Monitoring the pandemic: A fractional filter for the COVID-19 contact rate," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242380, Verein für Socialpolitik / German Economic Association.
    66. Gyula Seres & Anna Helen Balleyer & Nicola Cerutti & Anastasia Danilov & Jana Friedrichsen & Yiming Liu & Müge Süer, 2021. "Face masks increase compliance with physical distancing recommendations during the COVID-19 pandemic," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 7(2), pages 139-158, December.
    67. Matheus Pereira Libório & Petr Iakovlevitch Ekel & Carlos Augusto Paiva Martins, 2023. "Economic analysis through alternative data and big data techniques: what do they tell about Brazil?," SN Business & Economics, Springer, vol. 3(1), pages 1-16, January.
    68. Difang Huang & Jiti Gao & Tatsushi Oka, 2022. "Semiparametric Single-Index Estimation for Average Treatment Effects," Monash Econometrics and Business Statistics Working Papers 10/22, Monash University, Department of Econometrics and Business Statistics.
    69. Zubarev, Andrei & Kirillova, Maria, 2022. "Modeling COVID-19 spread in the Russian Federation using global VAR approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 65, pages 117-138.
    70. Jonas E. Arias & Jesús Fernández-Villaverde & Juan Rubio Ramírez & Minchul Shin, 2021. "The Causal Effects of Lockdown Policies on Health and Macroeconomic Outcomes," NBER Working Papers 28617, National Bureau of Economic Research, Inc.
    71. Daniel Goetz, 2022. "Does providing free internet access to low‐income households affect COVID‐19 spread?," Health Economics, John Wiley & Sons, Ltd., vol. 31(12), pages 2648-2663, December.
    72. Kos, Nenad & Fukuda, Satoshi & Carnehl, Christoph, 2021. "Epidemics With Behavior," CEPR Discussion Papers 16429, C.E.P.R. Discussion Papers.
    73. Martin Huber & Henrika Langen, 2020. "Timing matters: the impact of response measures on COVID-19-related hospitalization and death rates in Germany and Switzerland," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 156(1), pages 1-19, December.
    74. Anand Chopra & Michael B. Devereux & Amartya Lahiri, 2020. "Pandemics Through the Lens of Occupations," NBER Working Papers 27841, National Bureau of Economic Research, Inc.
    75. Léa BOU SLEIMAN & Germain GAUTHIER, 2020. "COVID-19: Reduced forms have gone viral, but what do they tell us?," Working Papers 2020-32, Center for Research in Economics and Statistics, revised 18 Jan 2021.
    76. Dion Bongaerts & Francesco Mazzola & Wolf Wagner, 2021. "Closed for business: The mortality impact of business closures during the Covid-19 pandemic," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-17, May.
    77. Ewen Gallic & Michel Lubrano & Pierre Michel, 2022. "Optimal lockdowns for COVID‐19 pandemics: Analyzing the efficiency of sanitary policies in Europe," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 24(5), pages 944-967, October.
    78. Florian Dorn & Sahamoddin Khailaie & Marc Stoeckli & Sebastian C. Binder & Tanmay Mitra & Berit Lange & Stefan Lautenbacher & Andreas Peichl & Patrizio Vanella & Timo Wollmershäuser & Clemens Fuest & , 2023. "The common interests of health protection and the economy: evidence from scenario calculations of COVID-19 containment policies," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(1), pages 67-74, February.
    79. Peter A.G. van Bergeijk, 2021. "Pandemic Economics," Books, Edward Elgar Publishing, number 20401.
    80. Zhaohui Su, 2021. "Rigorous Policy-Making Amid COVID-19 and Beyond: Literature Review and Critical Insights," IJERPH, MDPI, vol. 18(23), pages 1-17, November.
    81. Difang Huang & Ying Liang & Boyao Wu & Yanyi Ye, 2024. "Estimating the Impact of Social Distance Policy in Mitigating COVID-19 Spread with Factor-Based Imputation Approach," Papers 2405.12180, arXiv.org.
    82. Millimet, Daniel L. & Parmeter, Christopher F., 2021. "COVID-19 Severity: A New Approach to Quantifying Global Cases and Deaths," IZA Discussion Papers 14116, Institute of Labor Economics (IZA).
    83. Blas A. Marin-Lopez & David Jimenez-Gomez & José-María Abellán-Perpiñán, 2022. "Behavioral Economics in the Epidemiology of the COVID-19 Pandemic: Theory and Simulations," IJERPH, MDPI, vol. 19(15), pages 1-23, August.
    84. Germain Gauthier, 2021. "On the Use of Two-Way Fixed Effects Models for Policy Evaluation During Pandemics," Papers 2106.10949, arXiv.org.
    85. Cooper, Daniel & Garga, Vaishali & Luengo-Prado, María José & Tang, Jenny, 2023. "The mitigating effect of masks on the spread of Covid-19," Economics & Human Biology, Elsevier, vol. 48(C).
    86. Girardi, Alessandro & Ventura, Marco, 2023. "The cost of waiting and the death toll in Italy during the first wave of the covid-19 pandemic," Health Policy, Elsevier, vol. 134(C).
    87. Dhaval Dave & Drew McNichols & Joseph J. Sabia, 2022. "Political violence, risk aversion, and population health: Evidence from the US Capitol riot," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(4), pages 1345-1384, October.
    88. Nada Wasi, 2022. "Comment on “Responses to COVID‐19 in Southeast Asia: Diverse Paths and Ongoing Challenges”," Asian Economic Policy Review, Japan Center for Economic Research, vol. 17(1), pages 111-113, January.
    89. Bisin, Alberto & Moro, Andrea, 2022. "JUE insight: Learning epidemiology by doing: The empirical implications of a Spatial-SIR model with behavioral responses," Journal of Urban Economics, Elsevier, vol. 127(C).
    90. Hensel, Lukas & Witte, Marc & Caria, A. Stefano & Fetzer, Thiemo & Fiorin, Stefano & Götz, Friedrich M. & Gomez, Margarita & Haushofer, Johannes & Ivchenko, Andriy & Kraft-Todd, Gordon & Reutskaja, El, 2022. "Global Behaviors, Perceptions, and the Emergence of Social Norms at the Onset of the COVID-19 Pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 193(C), pages 473-496.
    91. Wei Tian & Seojeong Lee & Valentyn Panchenko, 2023. "Synthetic Controls with Multiple Outcomes," Papers 2304.02272, arXiv.org, revised Jul 2024.
    92. Fezzi, Carlo & Fanghella, Valeria, 2021. "Tracking GDP in real-time using electricity market data: Insights from the first wave of COVID-19 across Europe," European Economic Review, Elsevier, vol. 139(C).
    93. Greenhalgh, Trisha & Engebretsen, Eivind, 2022. "The science-policy relationship in times of crisis: An urgent call for a pragmatist turn," Social Science & Medicine, Elsevier, vol. 306(C).
    94. Gaggero, Alessio & Mesa-Pedrazas, Ángela & Fernández-Pérez, Ángel, 2024. "Shutting down to save lives: A regression discontinuity analysis of non-essential business closure," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    95. Christopher Avery & William Bossert & Adam Clark & Glenn Ellison & Sara Fisher Ellison, 2020. "An Economist's Guide to Epidemiology Models of Infectious Disease," Journal of Economic Perspectives, American Economic Association, vol. 34(4), pages 79-104, Fall.
    96. Jiayin Pei & Zhi Lu & Xiaoming Yang, 2022. "What drives people to repost social media messages during the COVID‐19 pandemic? Evidence from the Weibo news microblog," Growth and Change, Wiley Blackwell, vol. 53(4), pages 1609-1626, December.
    97. Sumedha Gupta & Kosali I. Simon & Coady Wing, 2020. "Mandated and Voluntary Social Distancing During The COVID-19 Epidemic: A Review," NBER Working Papers 28139, National Bureau of Economic Research, Inc.
    98. Marlon Tracey & Alicia Plemmons & Ariel Belasen, 2022. "Throwing caution to the wind: How hurricanes affect COVID‐19 spread," Health Economics, John Wiley & Sons, Ltd., vol. 31(6), pages 1258-1265, June.
    99. Jeroen Luyten & Sandy Tubeuf & Roselinde Kessels, 2022. "Rationing of a scarce life‐saving resource: Public preferences for prioritizing COVID‐19 vaccination," Health Economics, John Wiley & Sons, Ltd., vol. 31(2), pages 342-362, February.
    100. Byrne,Kieran Thomas & Kondylis,Florence & Loeser,John Ashton & Mukama,Denis, 2022. "A Few Good Masks : Evidence from Mask Manufacturing in Rwanda during the COVID-19 Pandemic," Policy Research Working Paper Series 9993, The World Bank.
    101. Michael Berlemann & Erik Haustein, 2020. "Right and Yet Wrong: A Spatio-Temporal Evaluation of Germany's Covid-19 Containment Policy," CESifo Working Paper Series 8446, CESifo.
    102. Olivia S. Kim & Jonathan A. Parker & Antoinette Schoar, 2020. "Revenue Collapses and the Consumption of Small Business Owners in the Early Stages of the COVID-19 Pandemic," NBER Working Papers 28151, National Bureau of Economic Research, Inc.
    103. Tobias Hartl, 2021. "Monitoring the pandemic: A fractional filter for the COVID-19 contact rate," Papers 2102.10067, arXiv.org.
    104. Michael D. Noel, 2022. "Competitive survival in a devastated industry: Evidence from hotels during COVID‐19," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(1), pages 3-24, February.
    105. Crudu, Federico & Di Stefano, Roberta & Mellace, Giovanni & Tiezzi, Silvia, 2024. "The gray zone: How not imposing a strict lockdown at the beginning of a pandemic can cost many lives," Labour Economics, Elsevier, vol. 89(C).
    106. Ron S. Kenett & Giancarlo Manzi & Carmit Rapaport & Silvia Salini, 2022. "Integrated Analysis of Behavioural and Health COVID-19 Data Combining Bayesian Networks and Structural Equation Models," IJERPH, MDPI, vol. 19(8), pages 1-26, April.
    107. Carlos B. Carneiro & I'uri H. Ferreira & Marcelo C. Medeiros & Henrique F. Pires & Eduardo Zilberman, 2020. "Lockdown effects in US states: an artificial counterfactual approach," Papers 2009.13484, arXiv.org, revised Feb 2021.
    108. Syed Abdul Rehman Khan & Danish Iqbal Godil & Munaza Bibi & Zhang Yu & Syed Muhammad Ahsan Rizvi, 2021. "The Economic and Social Impact of Teleworking in Romania: Present Practices and Post Pandemic Developments," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(58), pages 787-787, August.
    109. Lash, Michael T. & Sajeesh, S. & Araz, Ozgur M., 2023. "Predicting mobility using limited data during early stages of a pandemic," Journal of Business Research, Elsevier, vol. 157(C).
    110. Martin Kahanec & Lukáš Lafférs & Bernhard Schmidpeter, 2021. "The impact of repeated mass antigen testing for COVID-19 on the prevalence of the disease," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(4), pages 1105-1140, October.
    111. Mauro Caselli & Andrea Fracasso & Sergio Scicchitano, 2022. "From the lockdown to the new normal: individual mobility and local labor market characteristics following the COVID-19 pandemic in Italy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(4), pages 1517-1550, October.
    112. Martina Luskova, 2024. "The Effect of Face Masks on Covid Transmission: A Meta-Analysis," Working Papers IES 2024/2, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jan 2024.
    113. Philipp Breidenbach & Timo Mitze, 2022. "Large-scale sport events and COVID-19 infection effects: evidence from the German professional football ‘experiment’," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 15-45.
    114. Amodio, Emanuele & Battisti, Michele & Kourtellos, Andros & Maggio, Giuseppe & Maida, Carmelo Massimo, 2022. "Schools opening and Covid-19 diffusion: Evidence from geolocalized microdata," European Economic Review, Elsevier, vol. 143(C).
    115. Lipić, Tomislav & Štajduhar, Andrija & Medvidović, Luka & Wild, Dorian & Korošak, Dean & Podobnik, Boris, 2022. "Stringency without efficiency is not adequate to combat pandemics," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    116. Esaka, Taro & Fujii, Takao, 2022. "Quantifying the impact of the Tokyo Olympics on COVID-19 cases using synthetic control methods," Journal of the Japanese and International Economies, Elsevier, vol. 66(C).

  22. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.

    Cited by:

    1. AmirEmad Ghassami & Andrew Ying & Ilya Shpitser & Eric Tchetgen Tchetgen, 2021. "Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference," Papers 2104.02929, arXiv.org, revised Mar 2022.
    2. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2023. "Source Condition Double Robust Inference on Functionals of Inverse Problems," Papers 2307.13793, arXiv.org.
    3. Ravi B. Sojitra & Vasilis Syrgkanis, 2024. "Dynamic Local Average Treatment Effects," Papers 2405.01463, arXiv.org, revised May 2024.
    4. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2022. "Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals," Papers 2203.13887, arXiv.org, revised Jun 2023.
    5. Masahiro Kato & Masaaki Imaizumi & Kenichiro McAlinn & Haruo Kakehi & Shota Yasui, 2021. "Learning Causal Models from Conditional Moment Restrictions by Importance Weighting," Papers 2108.01312, arXiv.org, revised Sep 2022.
    6. Rahul Singh, 2021. "Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimension," Papers 2102.11076, arXiv.org, revised Jul 2024.
    7. Qizhao Chen & Morgane Austern & Vasilis Syrgkanis, 2023. "Inference on Optimal Dynamic Policies via Softmax Approximation," Papers 2303.04416, arXiv.org, revised Dec 2023.
    8. Wenlong Ji & Lihua Lei & Asher Spector, 2023. "Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects," Papers 2310.08115, arXiv.org, revised Nov 2024.
    9. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.
    10. Jonas Metzger, 2022. "Adversarial Estimators," Papers 2204.10495, arXiv.org, revised Jun 2022.
    11. Anish Agarwal & Rahul Singh, 2021. "Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy," Papers 2107.02780, arXiv.org, revised Feb 2024.
    12. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2021. "A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees," Papers 2105.15197, arXiv.org, revised Oct 2022.
    13. Xiaohong Chen & Yuan Liao & Weichen Wang, 2022. "Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves," Papers 2301.00092, arXiv.org, revised Jan 2023.
    14. Qizhao Chen & Vasilis Syrgkanis & Morgane Austern, 2022. "Debiased Machine Learning without Sample-Splitting for Stable Estimators," Papers 2206.01825, arXiv.org, revised Nov 2022.
    15. Dmitry Arkhangelsky & Guido W. Imbens & Lihua Lei & Xiaoman Luo, 2021. "Design-Robust Two-Way-Fixed-Effects Regression For Panel Data," Papers 2107.13737, arXiv.org, revised Mar 2024.
    16. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests," Papers 2110.03031, arXiv.org, revised Jun 2022.
    17. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression: Long Term, Mediated, and Time Varying Treatment Effects," Papers 2112.14249, arXiv.org, revised Mar 2024.

  23. Philipp Bach & Victor Chernozhukov & Martin Spindler, 2020. "Insights from Optimal Pandemic Shielding in a Multi-Group SEIR Framework," Papers 2011.01092, arXiv.org.

    Cited by:

    1. Milstein, Ricarda & Schreyögg, Jonas, 2022. "Activity-based funding based on diagnosis-related groups: The end of an era? A review of payment reforms in the inpatient sector in ten high-income countries," hche Research Papers 28, University of Hamburg, Hamburg Center for Health Economics (hche).

  24. Shuowen Chen & Victor Chernozhukov & Iv'an Fern'andez-Val, 2019. "Mastering Panel 'Metrics: Causal Impact of Democracy on Growth," Papers 1901.03821, arXiv.org.

    Cited by:

    1. Arindrajit Dube & Daniele Girardi & Òscar Jordà & Alan M. Taylor, 2023. "A Local Projections Approach to Difference-in-Differences Event Studies," Working Paper Series 2023-12, Federal Reserve Bank of San Francisco.
    2. Daniel Czarnowske & Amrei Stammann, 2020. "Inference in Unbalanced Panel Data Models with Interactive Fixed Effects," Papers 2004.03414, arXiv.org.
    3. Emanuele Amodio & Michele Battisti & Antonio Francesco Gravina & Andrea Mario Lavezzi & Giuseppe Maggio, 2023. "School‐age vaccination, school openings and Covid‐19 diffusion," Health Economics, John Wiley & Sons, Ltd., vol. 32(5), pages 1084-1100, May.
    4. Hryshko, Dmytro & Manovskii, Iourii, 2022. "How much consumption insurance in the U.S.?," Journal of Monetary Economics, Elsevier, vol. 130(C), pages 17-33.
    5. Mountford, Andrew, 2022. "Economic Growth Analysis When Balanced Growth Paths May Be Time Varying," MPRA Paper 114249, University Library of Munich, Germany.
    6. Andrew Chia, 2021. "Automatically Differentiable Random Coefficient Logistic Demand Estimation," Papers 2106.04636, arXiv.org.
    7. Victor Chernozhukov & Hiroyuki Kasahara & Paul Schrimpf, 2020. "Causal impact of masks, policies, behavior on early COVID-19 pandemic in the U.S," CeMMAP working papers CWP24/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Christoph Doerffel & Peter Draper & Andreas Freytag & Sebastian Schuhmann, 2021. "Drivers of Inclusive Development: An Empirical Investigation," Jena Economics Research Papers 2021-015, Friedrich-Schiller-University Jena.
    9. Ihsaan Bassier & Arindrajit Dube & Suresh Naidu, 2020. "Monopsony in Movers: The Elasticity of Labor Supply to Firm Wage Policies," NBER Working Papers 27755, National Bureau of Economic Research, Inc.
    10. Fontanari, Claudia, 2024. "The role of wages in triggering innovation and productivity: A dynamic exploration for European economies," Economic Modelling, Elsevier, vol. 130(C).

  25. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2019. "Inference on average treatment effects in aggregate panel data settings," CeMMAP working papers CWP32/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Masahiro Kato & Akari Ohda & Masaaki Imaizumi, 2023. "Asymptotically Unbiased Synthetic Control Methods by Distribution Matching," Papers 2307.11127, arXiv.org, revised May 2024.
    2. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2019. "Synthetic Difference In Differences," NBER Working Papers 25532, National Bureau of Economic Research, Inc.

  26. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2019. "Improved Central Limit Theorem and bootstrap approximations in high dimensions," Papers 1912.10529, arXiv.org, revised May 2022.

    Cited by:

    1. Magne Mogstad & Joseph P. Romano & Daniel Wilhelm & Azeem M. Shaikh, 2020. "Inference for ranks with applications to mobility across neighborhoods and academic achievement across countries," CeMMAP working papers CWP10/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
    3. Kock, Anders Bredahl & Preinerstorfer, David, 2024. "A remark on moment-dependent phase transitions in high-dimensional Gaussian approximations," Statistics & Probability Letters, Elsevier, vol. 211(C).
    4. Nan Liu & Yanbo Liu & Yuya Sasaki, 2024. "Estimation and Inference for Causal Functions with Multiway Clustered Data," Papers 2409.06654, arXiv.org.
    5. Matias D. Cattaneo & Ricardo P. Masini & William G. Underwood, 2022. "Yurinskii's Coupling for Martingales," Papers 2210.00362, arXiv.org, revised Sep 2024.
    6. Jinyuan Chang & Qing Jiang & Xiaofeng Shao, 2022. "Testing the martingale difference hypothesis in high dimension," Papers 2209.04770, arXiv.org, revised Sep 2022.
    7. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2022. "High-dimensional Data Bootstrap," Papers 2205.09691, arXiv.org.
    8. Cheng, Guanghui & Liu, Zhi & Peng, Liuhua, 2022. "Gaussian approximations for high-dimensional non-degenerate U-statistics via exchangeable pairs," Statistics & Probability Letters, Elsevier, vol. 182(C).
    9. Matias D. Cattaneo & Rajita Chandak & Michael Jansson & Xinwei Ma, 2022. "Boundary Adaptive Local Polynomial Conditional Density Estimators," Papers 2204.10359, arXiv.org, revised Dec 2023.
    10. Peccati, Giovanni & Turchi, Nicola, 2023. "The discrepancy between min–max statistics of Gaussian and Gaussian-subordinated matrices," Stochastic Processes and their Applications, Elsevier, vol. 158(C), pages 315-341.
    11. Kojevnikov, Denis & Song, Kyungchul, 2022. "A Berry–Esseen bound for vector-valued martingales," Statistics & Probability Letters, Elsevier, vol. 186(C).
    12. David M. Ritzwoller & Vasilis Syrgkanis, 2024. "Simultaneous Inference for Local Structural Parameters with Random Forests," Papers 2405.07860, arXiv.org, revised Sep 2024.

  27. Victor Chernozhukov & Iv'an Fern'andez-Val & Blaise Melly, 2019. "Fast Algorithms for the Quantile Regression Process," Papers 1909.05782, arXiv.org, revised Apr 2020.

    Cited by:

    1. Cuesta, Lizeth & Ruiz, Yomara, 2021. "Efecto de la globalización sobre la desigualdad. Un estudio global para 104 países usando regresiones cuantílicas [Effect of globalization on inequality. A global study for 104 countries using quan," MPRA Paper 111022, University Library of Munich, Germany.
    2. Wang, Xiuhua & Wang, Yipeng & Zhao, Yaxiong, 2022. "Financial permeation and rural poverty reduction Nexus: Further insights from counties in China," China Economic Review, Elsevier, vol. 76(C).
    3. Gloria Gonzalez-Rivera & Vladimir Rodriguez-Caballero & Esther Ruiz, 2023. "Expecting the unexpected: Stressed scenarios for economic growth," Working Papers 202314, University of California at Riverside, Department of Economics.
    4. Ignacio Garr'on & C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "International vulnerability of inflation," Papers 2410.20628, arXiv.org, revised Oct 2024.
    5. Garrón Vedia, Ignacio & Rodríguez Caballero, Carlos Vladimir & Ruiz Ortega, Esther, 2024. "International vulnerability of inflation," DES - Working Papers. Statistics and Econometrics. WS 44814, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Borgen, Nicolai T. & Haupt, Andreas & Wiborg, Øyvind N., 2021. "Flexible and fast estimation of quantile treatment effects: The rqr and rqrplot commands," SocArXiv 4vquh, Center for Open Science.
    7. Santiago Pereda-Fern'andez, 2024. "Fast Algorithms for Quantile Regression with Selection," Papers 2402.16693, arXiv.org.
    8. Sokbae Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2022. "Fast Inference for Quantile Regression with Tens of Millions of Observations," Papers 2209.14502, arXiv.org, revised Oct 2023.

  28. Victor Chernozhukov & Jerry Hausman & Whitney K. Newey, 2019. "Demand analysis with many prices," CeMMAP working papers CWP59/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    2. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Dubois, Pierre & Griffith, Rachel & O'Connell, Martin, 2017. "How well targeted are soda taxes?," CEPR Discussion Papers 12484, C.E.P.R. Discussion Papers.
    4. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.
    5. O'Connell, Martin & Smith, Kate, 2020. "Corrective Tax Design and Market Power," CEPR Discussion Papers 14582, C.E.P.R. Discussion Papers.
    6. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application," Papers 2008.03600, arXiv.org, revised Nov 2021.
    7. Martin O'Connell & Kate Smith, 2021. "Optimal sin taxation and market power," IFS Working Papers W21/30, Institute for Fiscal Studies.
    8. Ming Li, 2021. "Identification and Estimation in a Time-Varying Endogenous Random Coefficient Panel Data Model," Papers 2110.00982, arXiv.org, revised Nov 2024.
    9. Lin, Xirong, 2023. "Food demand and cash transfers: A collective household approach with Homescan data," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 233-259.

  29. Alexandre Belloni & Federico A. Bugni & Victor Chernozhukov, 2019. "Subvector inference in PI models with many moment inequalities," CeMMAP working papers CWP28/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Victor Aguirregabiria & Allan Collard-Wexler & Stephen P. Ryan, 2021. "Dynamic Games in Empirical Industrial Organization," Papers 2109.01725, arXiv.org, revised Sep 2021.
    2. Marcoux, Mathieu & Russell, Thomas M. & Wan, Yuanyuan, 2024. "A simple specification test for models with many conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 242(1).
    3. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.
    4. Thomas M. Russell, 2020. "Policy Transforms and Learning Optimal Policies," Papers 2012.11046, arXiv.org.

  30. Arun Chandrasekhar & Victor Chernozhukov & Francesca Molinari & Paul Schrimpf, 2019. "Best linear approximations to set identified functions: with an application to the gender wage gap," CeMMAP working papers 09/19, Institute for Fiscal Studies.

    Cited by:

    1. Wunsch, Conny & Strittmatter, Anthony, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," CEPR Discussion Papers 15840, C.E.P.R. Discussion Papers.
    2. , 2023. "Price Competition and Endogenous Product Choice in Networks: Evidence from the US airline Industry," Working Papers 950, Queen Mary University of London, School of Economics and Finance.

  31. Victor Chernozhukov & Whitney Newey & Vira Semenova, 2019. "Welfare Analysis in Dynamic Models," Papers 1908.09173, arXiv.org, revised Nov 2024.

    Cited by:

    1. Minkyung Kim & K. Sudhir & Kosuke Uetake, 2022. "A Structural Model of a Multitasking Salesforce: Incentives, Private Information, and Job Design," Management Science, INFORMS, vol. 68(6), pages 4602-4630, June.

  32. Victor Chernozhukov & Christian Hansen & Yuan Liao & Yinchu Zhu, 2019. "Inference for heterogeneous effects using low-rank estimations," CeMMAP working papers CWP31/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Miao, Ke & Phillips, Peter C.B. & Su, Liangjun, 2023. "High-dimensional VARs with common factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 155-183.
    2. Choi, Jungjun & Kwon, Hyukjun & Liao, Yuan, 2024. "Inference for low-rank completion without sample splitting with application to treatment effect estimation," Journal of Econometrics, Elsevier, vol. 240(1).
    3. Belloni, Alexandre & Chen, Mingli & Madrid Padilla, Oscar Hernan & Wang, Zixuan (Kevin), 2019. "High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing," The Warwick Economics Research Paper Series (TWERPS) 1230, University of Warwick, Department of Economics.
    4. Junlong Feng, 2019. "Regularized Quantile Regression with Interactive Fixed Effects," Papers 1911.00166, arXiv.org, revised Mar 2021.
    5. Yiren Wang & Liangjun Su & Yichong Zhang, 2022. "Low-rank Panel Quantile Regression: Estimation and Inference," Papers 2210.11062, arXiv.org.
    6. Jungjun Choi & Hyukjun Kwon & Yuan Liao, 2023. "Inference for Low-rank Completion without Sample Splitting with Application to Treatment Effect Estimation," Papers 2307.16370, arXiv.org.
    7. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.

  33. Shuowen Chen & Victor Chernozhukov & Iv'an Fern'andez-Val & Ye Luo, 2019. "SortedEffects: Sorted Causal Effects in R," Papers 1909.00836, arXiv.org, revised Nov 2019.

    Cited by:

    1. Diego Marino Fages, 2023. "Migration and trust: Evidence on assimilation from internal migrants," Discussion Papers 2023-08, Nottingham Interdisciplinary Centre for Economic and Political Research (NICEP).

  34. Mert Demirer & Vasilis Syrgkanis & Greg Lewis & Victor Chernozhukov, 2019. "Semi-Parametric Efficient Policy Learning with Continuous Actions," Papers 1905.10116, arXiv.org, revised Jul 2019.

    Cited by:

    1. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    2. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Yi Zhang & Kosuke Imai, 2023. "Individualized Policy Evaluation and Learning under Clustered Network Interference," Papers 2311.02467, arXiv.org, revised Feb 2024.
    4. AmirEmad Ghassami & Andrew Ying & Ilya Shpitser & Eric Tchetgen Tchetgen, 2021. "Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference," Papers 2104.02929, arXiv.org, revised Mar 2022.
    5. Masahiro Kato & Masatoshi Uehara & Shota Yasui, 2020. "Off-Policy Evaluation and Learning for External Validity under a Covariate Shift," Papers 2002.11642, arXiv.org, revised Oct 2020.
    6. Masahiro Kato, 2020. "Confidence Interval for Off-Policy Evaluation from Dependent Samples via Bandit Algorithm: Approach from Standardized Martingales," Papers 2006.06982, arXiv.org.
    7. Zhan, Ruohan & Ren, Zhimei & Athey, Susan & Zhou, Zhengyuan, 2021. "Policy Learning with Adaptively Collected Data," Research Papers 3963, Stanford University, Graduate School of Business.
    8. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Chunrong Ai & Yue Fang & Haitian Xie, 2024. "Data-driven Policy Learning for Continuous Treatments," Papers 2402.02535, arXiv.org, revised Nov 2024.
    10. Andrew Bennett & Nathan Kallus, 2020. "Efficient Policy Learning from Surrogate-Loss Classification Reductions," Papers 2002.05153, arXiv.org.
    11. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    12. Ruohan Zhan & Shichao Han & Yuchen Hu & Zhenling Jiang, 2024. "Estimating Treatment Effects under Recommender Interference: A Structured Neural Networks Approach," Papers 2406.14380, arXiv.org, revised Jul 2024.
    13. Cai, Hengrui & Shi, Chengchun & Song, Rui & Lu, Wenbin, 2023. "Jump interval-learning for individualized decision making with continuous treatments," LSE Research Online Documents on Economics 118231, London School of Economics and Political Science, LSE Library.
    14. Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2020. "Deep Learning for Individual Heterogeneity: An Automatic Inference Framework," Papers 2010.14694, arXiv.org, revised Jul 2021.

  35. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2019. "Distributional conformal prediction," Papers 1909.07889, arXiv.org, revised Aug 2021.

    Cited by:

    1. Leying Guan, 2023. "Localized conformal prediction: a generalized inference framework for conformal prediction," Biometrika, Biometrika Trust, vol. 110(1), pages 33-50.

  36. Victor Chernozhukov & Vira Semenova, 2018. "Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions," CeMMAP working papers CWP40/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Waverly Wei & Maya Petersen & Mark J van der Laan & Zeyu Zheng & Chong Wu & Jingshen Wang, 2023. "Efficient targeted learning of heterogeneous treatment effects for multiple subgroups," Biometrics, The International Biometric Society, vol. 79(3), pages 1934-1946, September.
    2. Michael Zimmert & Michael Lechner, 2019. "Nonparametric estimation of causal heterogeneity under high-dimensional confounding," Papers 1908.08779, arXiv.org.
    3. Daniel Jacob, 2019. "Group Average Treatment Effects for Observational Studies," Papers 1911.02688, arXiv.org, revised Mar 2020.
    4. Manisha Shah & Sarah Baird & Jennifer Seager & Benjamin Avuwadah & Joan Hamory & Shwetlena Sabarwal & Amita Vyas, 2024. "Improving Mental Health of Adolescent Girls in Low- and Middle-Income Countries: Causal Evidence from Life Skills Programming," Journal of Human Resources, University of Wisconsin Press, vol. 59(S), pages 317-364.
    5. Jacob, Daniel & Härdle, Wolfgang Karl & Lessmann, Stefan, 2019. "Group Average Treatment Effects for Observational Studies," IRTG 1792 Discussion Papers 2019-028, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

  37. Philipp Bach & Victor Chernozhukov & Martin Spindler, 2018. "Valid Simultaneous Inference in High-Dimensional Settings (with the hdm package for R)," Papers 1809.04951, arXiv.org.

    Cited by:

    1. Helmut Wasserbacher & Martin Spindler, 2024. "Credit Ratings: Heterogeneous Effect on Capital Structure," Papers 2406.18936, arXiv.org.
    2. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2022. "High-dimensional Data Bootstrap," Papers 2205.09691, arXiv.org.
    3. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    4. Barbara Felderer & Jannis Kueck & Martin Spindler, 2021. "Big Data meets Causal Survey Research: Understanding Nonresponse in the Recruitment of a Mixed-mode Online Panel," Papers 2102.08994, arXiv.org.

  38. Alexandre Belloni & Federico Bugni & Victor Chernozhukov, 2018. "Subvector Inference in Partially Identified Models with Many Moment Inequalities," Papers 1806.11466, arXiv.org.

    Cited by:

    1. Chesher, Andrew & Kim, Dongwoo & Rosen, Adam M., 2023. "IV methods for Tobit models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1700-1724.
    2. Arie Beresteanu, 2020. "Quantile Regression with Interval Data," Working Paper 6899, Department of Economics, University of Pittsburgh.
    3. Moyu Liao, 2020. "Estimating Economic Models with Testable Assumptions: Theory and Applications," Papers 2002.10415, arXiv.org, revised Mar 2022.
    4. Francesca Molinari, 2020. "Microeconometrics with Partial Identi?cation," CeMMAP working papers CWP15/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Isaiah Andrews & Jonathan Roth & Ariel Pakes, 2023. "Inference for Linear Conditional Moment Inequalities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(6), pages 2763-2791.
    6. Bei, Xinyue, 2024. "Local linearization based subvector inference in moment inequality models," Journal of Econometrics, Elsevier, vol. 238(1).

  39. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iván Fernández-Val, 2018. "Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," NBER Working Papers 24678, National Bureau of Economic Research, Inc.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    3. Abhijit Banerjee & Esther Duflo & Amy Finkelstein & Lawrence F. Katz & Benjamin A. Olken & Anja Sautmann, 2020. "In Praise of Moderation: Suggestions for the Scope and Use of Pre-Analysis Plans for RCTs in Economics," NBER Working Papers 26993, National Bureau of Economic Research, Inc.
    4. De Neve, Jan-Emmanuel & Imbert, Clement & Spinnewijn, Johannes & Tsankova, Teodora & Luts, Maarten, 2020. "How to Improve Tax Compliance? Evidence from Population-wide Experiments in Belgium," CAGE Online Working Paper Series 458, Competitive Advantage in the Global Economy (CAGE).
    5. Briole, Simon & Gurgand, Marc & Maurin, Eric & McNally, Sandra & Ruiz-Valenzuela, Jenifer & Santín, Daniel, 2022. "The Making of Civic Virtues: A School-Based Experiment in Three Countries," IZA Discussion Papers 15141, Institute of Labor Economics (IZA).
    6. Ubfal,Diego Javier, 2024. "What Works in Supporting Women-Led Businesses ?," Policy Research Working Paper Series 10744, The World Bank.
    7. Daniel Jacob, 2021. "CATE meets ML," Digital Finance, Springer, vol. 3(2), pages 99-148, June.
    8. M. Adam & O. Bonnet & E. Fize & T. Loisel & M. Rault & L. Wilner, 2023. "How does fuel demand respond to price changes? Quasi-experimental evidence based on high-frequency data," Documents de Travail de l'Insee - INSEE Working Papers 2023-17, Institut National de la Statistique et des Etudes Economiques.
    9. Dar, Manzoor H. & de Janvry, Alain & Emerick, Kyle & Kelley, Erin M. & Sadoulet, Elisabeth, 2019. "Endogenous Information Sharing and the Gains from Using Network Information to Maximize Technology Adoption," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt8qx7m4zq, Department of Agricultural & Resource Economics, UC Berkeley.
    10. Douglas Kiarelly Godoy de Araujo, 2023. "gingado: a machine learning library focused on economics and finance," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: applications and tools, volume 59, Bank for International Settlements.
    11. Abhijit Banerjee & Emily Breza & Esther Duflo & Cynthia Kinnan, 2019. "Can Microfinance Unlock a Poverty Trap for Some Entrepreneurs?," Discussion Papers Series, Department of Economics, Tufts University 0832, Department of Economics, Tufts University.
    12. Sendhil Mullainathan & Ashesh Rambachan, 2024. "From Predictive Algorithms to Automatic Generation of Anomalies," Papers 2404.10111, arXiv.org.
    13. Dmitry Arkhangelsky & Kazuharu Yanagimoto & Tom Zohar, 2024. "Flexible Analysis of Individual Heterogeneity in Event Studies: Application to the Child Penalty," Papers 2403.19563, arXiv.org.
    14. Sandrine Mesplé-Somps & Björn Nilsson, 2021. "Role models and migration intentions," Working Papers hal-03105639, HAL.
    15. Patrick Bajari & Zhihao Cen & Victor Chernozhukov & Manoj Manukonda & Jin Wang & Ramon Huerta & Junbo Li & Ling Leng & George Monokroussos & Suhas Vijaykunar & Shan Wan, 2023. "Hedonic prices and quality adjusted price indices powered by AI," CeMMAP working papers 08/23, Institute for Fiscal Studies.
    16. Stefano Piasenti & Marica Valente & Roel van Veldhuizen & Gregor Pfeifer & Gregor-Gabriel Pfeifer, 2023. "Does Unfairness Hurt Women? The Effects of Losing Unfair Competitions," CESifo Working Paper Series 10572, CESifo.
    17. Gallego, Francisco A. & Malamud, Ofer & Pop-Eleches, Cristian, 2020. "Parental monitoring and children's internet use: The role of information, control, and cues," Journal of Public Economics, Elsevier, vol. 188(C).
    18. Susan Athey & Katy Ann Bergstrom & Vitor Hadad & Julian C. Jamison & Berk Özler & Luca Parisotto & Julius Dohbit Sama, 2021. "Shared Decision-Making: Can Improved Counseling Increase Willingness to Pay for Modern Contraceptives?," Discussion Papers 2105, University of Exeter, Department of Economics.
    19. Elek, Péter & Bíró, Anikó, 2021. "Regional differences in diabetes across Europe – regression and causal forest analyses," Economics & Human Biology, Elsevier, vol. 40(C).
    20. Simon Briole & Augustin Colette & Emmanuelle Lavaine, 2023. "The Heterogeneous Effects of Lockdown Policies on Air Pollution," CEE-M Working Papers hal-04084912, CEE-M, Universtiy of Montpellier, CNRS, INRA, Montpellier SupAgro.
    21. Robin Gubela & Artem Bequé & Stefan Lessmann & Fabian Gebert, 2019. "Conversion Uplift in E-Commerce: A Systematic Benchmark of Modeling Strategies," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 747-791, May.
    22. Petru Crudu, 2023. "Long-term effects of early adverse labour market conditions: A Causal Machine Learning approach," Working Papers 2023:21, Department of Economics, University of Venice "Ca' Foscari".
    23. Phillip Heiler, 2022. "Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization," Papers 2209.04329, arXiv.org, revised Jul 2024.
    24. Keshav Agrawal & Susan Athey & Ayush Kanodia & Emil Palikot, 2022. "Personalized Recommendations in EdTech: Evidence from a Randomized Controlled Trial," Papers 2208.13940, arXiv.org, revised Dec 2022.
    25. Jonas Hjort & Vinayak Iyer & Golvine de Rochambeau, 2020. "Informational Barriers to Market Access: Experimental Evidence from Liberian Firms," Working Papers hal-03389180, HAL.
    26. Axenbeck, Janna & Berner, Anne & Kneib, Thomas, 2022. "What drives the relationship between digitalization and industrial energy demand? Exploring firm-level heterogeneity," ZEW Discussion Papers 22-059, ZEW - Leibniz Centre for European Economic Research.
    27. Pedro Forquesato, 2022. "Who Benefits from Political Connections in Brazilian Municipalities," Papers 2204.09450, arXiv.org.
    28. Conner Mullally & Mayra Rivas & Travis McArthur, 2021. "Using Machine Learning to Estimate the Heterogeneous Effects of Livestock Transfers," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1058-1081, May.
    29. Benjamin A. Olken, 2020. "Banerjee, Duflo, Kremer, and the Rise of Modern Development Economics," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(3), pages 853-878, July.
    30. Aparajithan Venkateswaran & Anirudh Sankar & Arun G. Chandrasekhar & Tyler H. McCormick, 2024. "Robustly estimating heterogeneity in factorial data using Rashomon Partitions," Papers 2404.02141, arXiv.org, revised Aug 2024.
    31. Jeffrey G. Shrader & Laura Bakkensen & Derek Lemoine, 2023. "Fatal Errors: The Mortality Value of Accurate Weather Forecasts," Working Papers 23-30, Center for Economic Studies, U.S. Census Bureau.
    32. Strittmatter, Anthony, 2019. "What is the Value Added by using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203499, Verein für Socialpolitik / German Economic Association.
    33. Muller, Seán M., 2020. "The implications of a fundamental contradiction in advocating randomized trials for policy," World Development, Elsevier, vol. 127(C).
    34. Julia Hatamyar & Noemi Kreif, 2023. "Policy Learning with Rare Outcomes," Papers 2302.05260, arXiv.org, revised Oct 2023.
    35. Martin Kroczek & Philipp Kugler, 2022. "Heterogeneous Effects of Monetary and Non-Monetary Job Characteristics on Job Attractiveness in Nursing," IAW Discussion Papers 139, Institut für Angewandte Wirtschaftsforschung (IAW).
    36. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    37. Mullally, Conner & Rivas, Mayra & McArthur, Travis, 2019. "Livestock Transfers Can Improve Child Health: Evidence from a Randomized Phase-In Design in Guatemala," SocArXiv c6zg5, Center for Open Science.
    38. A. Stefano Caria & Grant Gordon & Maximilian Kasy & Simon Quinn & Soha Shami & Alexander Teytelboym, 2020. "An Adaptive Targeted Field Experiment: Job Search Assistance for Refugees in Jordan," CSAE Working Paper Series 2020-20, Centre for the Study of African Economies, University of Oxford.
    39. Kroczek, Martin & Kugler, Philipp, 2022. "Heterogeneous Effects of Monetary and Non-Monetary Job Characteristics on Job Attractiveness in Nursing," VfS Annual Conference 2022 (Basel): Big Data in Economics 264108, Verein für Socialpolitik / German Economic Association.
    40. Markus Eyting, 2020. "A Random Forest a Day Keeps the Doctor Away," Working Papers 2026, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    41. Jeffrey Smith, 2022. "Treatment Effect Heterogeneity," Evaluation Review, , vol. 46(5), pages 652-677, October.
    42. David Glynn & John Giardina & Julia Hatamyar & Ankur Pandya & Marta Soares & Noemi Kreif, 2024. "Integrating decision modeling and machine learning to inform treatment stratification," Health Economics, John Wiley & Sons, Ltd., vol. 33(8), pages 1772-1792, August.
    43. David M. Ritzwoller & Joseph P. Romano, 2023. "Reproducible Aggregation of Sample-Split Statistics," Papers 2311.14204, arXiv.org, revised Nov 2024.
    44. Simon Calmar Andersen & Louise Beuchert & Phillip Heiler & Helena Skyt Nielsen, 2023. "A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health," Papers 2308.04963, arXiv.org.
    45. Minahil Asim & Ronak Jain & Vatsal Khandelwal, 2024. "Great expectations? Experimental evidence from schools in Pakistan," ECON - Working Papers 454, Department of Economics - University of Zurich.
    46. Nathan Kallus, 2022. "Treatment Effect Risk: Bounds and Inference," Papers 2201.05893, arXiv.org, revised Jul 2022.
    47. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    48. Liu, Lin & Mukherjee, Rajarshi & Robins, James M., 2024. "Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators," Journal of Econometrics, Elsevier, vol. 240(2).
    49. Petru Crudu & Giacomo Pasini, 2023. "The Health Burden of Job Strain: Evidence from Europe," Working Papers 2023:19, Department of Economics, University of Venice "Ca' Foscari".
    50. Chaisemartin, Clement de & Navarrete, Nicolas, 2019. "The direct and spillover effects of a mental health program for disruptive students," CAGE Online Working Paper Series 401, Competitive Advantage in the Global Economy (CAGE).
    51. Taiyo Fukai & Hidehiko Ichimura & Keisuke Kawata, 2021. "Describing the impacts of COVID-19 on the labor market in Japan until June 2020," The Japanese Economic Review, Springer, vol. 72(3), pages 439-470, July.
    52. Maria Nareklishvili & Nicholas Polson & Vadim Sokolov, 2022. "Feature Selection for Personalized Policy Analysis," Papers 2301.00251, arXiv.org, revised Jul 2023.
    53. Lily Davies & Mark Kattenberg & Benedikt Vogt, 2023. "Predicting Firm Exits with Machine Learning: Implications for Selection into COVID-19 Support and Productivity Growth," CPB Discussion Paper 444, CPB Netherlands Bureau for Economic Policy Analysis.
    54. Dorothee Weiffen & Ghassan Baliki & Tilman Brück, 2022. "Violent conflict moderates food security impacts of agricultural asset transfers in Syria: A heterogeneity analysis using machine learning," HiCN Working Papers 381, Households in Conflict Network.
    55. Kyle Myers & Wei Yang Tham, 2023. "Money, Time, and Grant Design," Papers 2312.06479, arXiv.org.
    56. Kretschmer, Sandra, 2024. "Carbon literacy – Can simple interventions help? Effect of information provision on emissions knowledge of private households," Energy Policy, Elsevier, vol. 188(C).

  40. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.

    Cited by:

    1. Andrii Babii & Xi Chen & Eric Ghysels & Rohit Kumar, 2020. "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice," Papers 2010.08463, arXiv.org, revised Nov 2021.
    2. Yoici Arai & Taisuke Otsu & Myung Hwan Seo, 2022. "Regression discontinuity design with potentially many covariates," STICERD - Econometrics Paper Series 626, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    3. Victor Chernozhukov & Chen Huang & Weining Wang, 2021. "Uniform Inference on High-dimensional Spatial Panel Networks," Papers 2105.07424, arXiv.org, revised Sep 2023.
    4. Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-38, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    5. Manu Navjeevan, 2023. "An Identification and Dimensionality Robust Test for Instrumental Variables Models," Papers 2311.14892, arXiv.org, revised Dec 2024.
    6. Chetverikov, Denis & Wilhelm, Daniel & Kim, Dongwoo, 2021. "An Adaptive Test Of Stochastic Monotonicity," Econometric Theory, Cambridge University Press, vol. 37(3), pages 495-536, June.
    7. Saulius Jokubaitis & Remigijus Leipus, 2022. "Asymptotic Normality in Linear Regression with Approximately Sparse Structure," Mathematics, MDPI, vol. 10(10), pages 1-28, May.
    8. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2019. "Improved Central Limit Theorem and bootstrap approximations in high dimensions," Papers 1912.10529, arXiv.org, revised May 2022.
    9. Baris Ata & Alexandre Belloni & Ozan Candogan, 2018. "Latent Agents in Networks: Estimation and Targeting," Papers 1808.04878, arXiv.org, revised Jan 2022.
    10. Andrii Babii, 2020. "High-dimensional mixed-frequency IV regression," Papers 2003.13478, arXiv.org.
    11. Byol Kim & Song Liu & Mladen Kolar, 2021. "Two‐sample inference for high‐dimensional Markov networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 939-962, November.
    12. Masayuki Sawada & Kohei Kawaguchi, 2020. "Estimating High-Dimensional Discrete Choice Model of Differentiated Products with Random Coefficients," Papers 2004.08791, arXiv.org.
    13. Chen, Bin & Maung, Kenwin, 2023. "Time-varying forecast combination for high-dimensional data," Journal of Econometrics, Elsevier, vol. 237(2).
    14. Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-50, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    15. Adam Baybutt & Manu Navjeevan, 2023. "Doubly-Robust Inference for Conditional Average Treatment Effects with High-Dimensional Controls," Papers 2301.06283, arXiv.org.
    16. Xinwei Ma & Jingshen Wang, 2018. "Robust Inference Using Inverse Probability Weighting," Papers 1810.11397, arXiv.org, revised May 2019.
    17. Dmitry Arkhangelsky & Vasily Korovkin, 2020. "On Policy Evaluation with Aggregate Time-Series Shocks," CERGE-EI Working Papers wp657, The Center for Economic Research and Graduate Education - Economics Institute, Prague.

  41. Victor Chernozhukov & Iv'an Fern'andez-Val & Siyi Luo, 2018. "Distribution Regression with Sample Selection, with an Application to Wage Decompositions in the UK," Papers 1811.11603, arXiv.org, revised Dec 2023.

    Cited by:

    1. Wunsch, Conny & Strittmatter, Anthony, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," CEPR Discussion Papers 15840, C.E.P.R. Discussion Papers.
    2. Miguel A Delgado & Andrés García-Suaza & Pedro H C Sant’Anna, 2022. "Distribution regression in duration analysis: an application to unemployment spells [Lecture notes in statistics: Proceedings]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 675-698.
    3. Liu, Ruixuan & Yu, Zhengfei, 2022. "Sample selection models with monotone control functions," Journal of Econometrics, Elsevier, vol. 226(2), pages 321-342.
    4. Dominik Wied, 2022. "Semiparametric Distribution Regression with Instruments and Monotonicity," Papers 2212.03704, arXiv.org.

  42. Victor Chernozhukov & Iv'an Fern'andez-Val & Sukjin Han & Amanda Kowalski, 2018. "Censored Quantile Instrumental Variable Estimation with Stata," Papers 1801.05305, arXiv.org, revised Sep 2019.

    Cited by:

    1. Kim, Young-Joo & Daly, Vincent, 2019. "The Education Gradient in Health: The Case of Obesity in the UK and US," Economics Discussion Papers 2019-4, School of Economics, Kingston University London.
    2. L. Benfratello & A. Bottasso & C. Piccardo, 2022. "R&D and export performance: exploring heterogeneity along the export intensity distribution," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(2), pages 189-232, June.
    3. Männasoo, Kadri, 2022. "Working hours and gender wage differentials: Evidence from the American Working Conditions Survey," Labour Economics, Elsevier, vol. 76(C).
    4. David M. Kaplan, 2020. "sivqr: Smoothed IV quantile regression," Working Papers 2009, Department of Economics, University of Missouri.
    5. Sanna Nivakoski, 2020. "Wealth and the effect of subjective survival probability," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(2), pages 633-670, April.
    6. Abe Dunn, 2014. "Health Insurance and the Demand for Medical Care: Instrumental Variable Estimates Using Health Insurer Claims Data," BEA Working Papers 0107, Bureau of Economic Analysis.
    7. Sugimoto, Kota, 2021. "Ownership versus legal unbundling of electricity transmission network: Evidence from renewable energy investment in Germany," Energy Economics, Elsevier, vol. 99(C).

  43. Xi Chen & Victor Chernozhukov & Iv'an Fern'andez-Val & Scott Kostyshak & Ye Luo, 2018. "Shape-Enforcing Operators for Point and Interval Estimators," Papers 1809.01038, arXiv.org, revised Feb 2021.

    Cited by:

    1. Zheng Fang & Juwon Seo, 2021. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Econometrica, Econometric Society, vol. 89(5), pages 2439-2458, September.
    2. Zheng Fang, 2021. "A Unifying Framework for Testing Shape Restrictions," Papers 2107.12494, arXiv.org, revised Aug 2021.
    3. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
    4. Harold D. Chiang & Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs," Papers 2102.06586, arXiv.org.

  44. Victor Chernozhukov & Denis Nekipelov & Vira Semenova & Vasilis Syrgkanis, 2018. "Plug-in regularized estimation of high dimensional parameters in nonlinear semiparametric models," CeMMAP working papers CWP41/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
    2. Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
    3. Sookyo Jeong & Hongseok Namkoong, 2020. "Assessing External Validity Over Worst-case Subpopulations," Papers 2007.02411, arXiv.org, revised Feb 2022.
    4. Jann Spiess & Vasilis Syrgkanis & Victor Yaneng Wang, 2021. "Finding Subgroups with Significant Treatment Effects," Papers 2103.07066, arXiv.org, revised Dec 2023.

  45. Denis Nekipelov & Vira Semenova & Vasilis Syrgkanis, 2018. "Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models," Papers 1806.04823, arXiv.org, revised Sep 2021.

    Cited by:

    1. Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
    2. Sookyo Jeong & Hongseok Namkoong, 2020. "Assessing External Validity Over Worst-case Subpopulations," Papers 2007.02411, arXiv.org, revised Feb 2022.
    3. Dylan J. Foster & Vasilis Syrgkanis, 2019. "Orthogonal Statistical Learning," Papers 1901.09036, arXiv.org, revised Jun 2023.

  46. Victor Chernozhukov & Whitney Newey & Rahul Singh, 2018. "De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers," Papers 1802.08667, arXiv.org, revised Oct 2022.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    3. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2020. "Unconditional Quantile Regression with High Dimensional Data," Papers 2007.13659, arXiv.org, revised Feb 2022.
    4. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    5. Gyungbae Park, 2024. "Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions," Papers 2403.15934, arXiv.org.
    6. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022. "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers 2211.07903, arXiv.org.
    8. Zhengyu Zhang & Zequn Jin & Lihua Lin, 2024. "Identification and inference of outcome conditioned partial effects of general interventions," Papers 2407.16950, arXiv.org.
    9. Liu, Lin & Mukherjee, Rajarshi & Robins, James M., 2024. "Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators," Journal of Econometrics, Elsevier, vol. 240(2).
    10. Antonio R. Linero, 2023. "Prior and posterior checking of implicit causal assumptions," Biometrics, The International Biometric Society, vol. 79(4), pages 3153-3164, December.
    11. David Bruns-Smith & Oliver Dukes & Avi Feller & Elizabeth L. Ogburn, 2023. "Augmented balancing weights as linear regression," Papers 2304.14545, arXiv.org, revised Jun 2024.

  47. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2018. "A $t$-test for synthetic controls," Papers 1812.10820, arXiv.org, revised Jan 2024.

    Cited by:

    1. Billy Ferguson & Brad Ross, 2020. "Assessing the Sensitivity of Synthetic Control Treatment Effect Estimates to Misspecification Error," Papers 2012.15367, arXiv.org, revised Feb 2021.
    2. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    3. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    4. Guido Imbens & Nathan Kallus & Xiaojie Mao, 2021. "Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions: From Two-Way Fixed Effects to Factor Models," Papers 2108.03849, arXiv.org.
    5. Li, Xingyu & Shen, Yan & Zhou, Qiankun, 2024. "Confidence intervals of treatment effects in panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 240(1).
    6. Bruno Ferman & Cristine Pinto, 2021. "Synthetic controls with imperfect pretreatment fit," Quantitative Economics, Econometric Society, vol. 12(4), pages 1197-1221, November.
    7. Erick Lahura & Rosario Sabrera, 2023. "The effect of infrastructure investment on tourism demand: a synthetic control approach for the case of Kuelap, Peru," Empirical Economics, Springer, vol. 65(1), pages 443-478, July.
    8. Nicolaj S{o}ndergaard Muhlbach & Mikkel Slot Nielsen, 2019. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," Papers 1909.03968, arXiv.org, revised Feb 2021.
    9. Anish Agarwal & Rahul Singh, 2021. "Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy," Papers 2107.02780, arXiv.org, revised Feb 2024.
    10. Jianfei Cao & Shirley Lu, 2019. "Synthetic Control Inference for Staggered Adoption: Estimating the Dynamic Effects of Board Gender Diversity Policies," Papers 1912.06320, arXiv.org.

  48. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2018. "Exact and robust conformal inference methods for predictive machine learning with dependent data," CeMMAP working papers CWP16/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Fantazzini, Dean, 2024. "Adaptive Conformal Inference for computing Market Risk Measures: an Analysis with Four Thousands Crypto-Assets," MPRA Paper 121214, University Library of Munich, Germany.
    2. Ajroldi, Niccolò & Diquigiovanni, Jacopo & Fontana, Matteo & Vantini, Simone, 2023. "Conformal prediction bands for two-dimensional functional time series," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    3. Borgschulte, Mark & Vogler, Jacob, 2019. "Did the ACA Medicaid Expansion Save Lives?," IZA Discussion Papers 12552, Institute of Labor Economics (IZA).
    4. Federico A. Bugni & Jia Li & Qiyuan Li, 2023. "Permutation‐based tests for discontinuities in event studies," Quantitative Economics, Econometric Society, vol. 14(1), pages 37-70, January.
    5. Chernozhukov, Victor & Wüthrich, Kaspar & Zhu, Yinchu, 2021. "Distributional conformal prediction," University of California at San Diego, Economics Working Paper Series qt2zs6m5p5, Department of Economics, UC San Diego.
    6. Viviano, Davide & Bradic, Jelena, 2023. "Synthetic Learner: Model-free inference on treatments over time," Journal of Econometrics, Elsevier, vol. 234(2), pages 691-713.
    7. Matteo Fontana & Gianluca Zeni & Simone Vantini, 2020. "Conformal Prediction: a Unified Review of Theory and New Challenges," Papers 2005.07972, arXiv.org, revised Jul 2022.
    8. Varun Gupta & Christopher Jung & Georgy Noarov & Mallesh M. Pai & Aaron Roth, 2021. "Online Multivalid Learning: Means, Moments, and Prediction Intervals," Papers 2101.01739, arXiv.org.

  49. Victor Chernozhukov & Iv'an Fern'andez-Val & Martin Weidner, 2018. "Network and Panel Quantile Effects Via Distribution Regression," Papers 1803.08154, arXiv.org, revised Jun 2020.

    Cited by:

    1. Botosaru, Irene & Muris, Chris & Pendakur, Krishna, 2023. "Identification of time-varying transformation models with fixed effects, with an application to unobserved heterogeneity in resource shares," Journal of Econometrics, Elsevier, vol. 232(2), pages 576-597.
    2. Alvarado, Rafael & Cuesta, Lizeth & Kumar, Pavan & Rehman, Abdul & Murshed, Muntasir & Işık, Cem & Vega, Nora & Ochoa-Moreno, Santiago & Tillaguango, Brayan, 2022. "Impact of natural resources on economic progress: Evidence for trading blocs in Latin America using non-linear econometric methods," Resources Policy, Elsevier, vol. 79(C).
    3. Neary, Peter & Carrère, Céline & Mrázová, Monika, 2020. "Gravity without Apologies: The Science of Elasticities, Distance, and Trade," CEPR Discussion Papers 14473, C.E.P.R. Discussion Papers.
    4. Peter Neary & Céline Carrère & Monika Mrázová, 2020. "Gravity without Apology: The Science of Elasticities, Distance, and Trade," Economics Series Working Papers 904, University of Oxford, Department of Economics.
    5. Ivan Fernandez-Val & Wayne Yuan Gao & Yuan Liao & Francis Vella, 2022. "Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes," Papers 2202.04154, arXiv.org, revised Jan 2023.
    6. Vu, Dung Anh & Van Nguyen, Thinh & Nhu, Quang Minh & Tran, Tuyen Quang, 2024. "Does increased digital transformation promote a firm's financial performance? New insights from the quantile approach," Finance Research Letters, Elsevier, vol. 64(C).
    7. Tatsushi Oka & Shota Yasui & Yuta Hayakawa & Undral Byambadalai, 2024. "Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trials," Papers 2407.14074, arXiv.org.
    8. Alnafrah, Ibrahim & Belyaeva, Zhanna, 2024. "The nonlinear road to happiness: Making sense of ESGD impacts on well-being," Structural Change and Economic Dynamics, Elsevier, vol. 70(C), pages 365-381.
    9. Andreas Dzemski, 2019. "An Empirical Model of Dyadic Link Formation in a Network with Unobserved Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 101(5), pages 763-776, December.
    10. Maike Hohberg & Peter Pütz & Thomas Kneib, 2020. "Treatment effects beyond the mean using distributional regression: Methods and guidance," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-29, February.
    11. Wang, Yunyun & Oka, Tatsushi & Zhu, Dan, 2023. "Bivariate distribution regression with application to insurance data," Insurance: Mathematics and Economics, Elsevier, vol. 113(C), pages 215-232.
    12. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2023. "Distributional Vector Autoregression: Eliciting Macro and Financial Dependence," Papers 2303.04994, arXiv.org.
    13. Nathan Kallus & Miruna Oprescu, 2022. "Robust and Agnostic Learning of Conditional Distributional Treatment Effects," Papers 2205.11486, arXiv.org, revised Feb 2023.

  50. Patrick Bajari & Victor Chernozhukov & Ali Hortaçsu & Junichi Suzuki, 2018. "The Impact of Big Data on Firm Performance: An Empirical Investigation," NBER Working Papers 24334, National Bureau of Economic Research, Inc.

    Cited by:

    1. Patrick Bajari & Victor Chernozhukov & Ali Hortaçsu & Junichi Suzuki, 2019. "The Impact of Big Data on Firm Performance: An Empirical Investigation," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 33-37, May.
    2. Elena Argentesi & Paolo Buccirossi & Emilio Calvano & Tomaso Duso & Alessia Marrazzo & Salvatore Nava, 2019. "Merger Policy in Digital Markets: An Ex-Post Assessment," CESifo Working Paper Series 7985, CESifo.
    3. Hamid Etemad, 2024. "Transformative potentials of generative artificial intelligence: Should international entrepreneurial enterprises adopt GEN.AI?," Journal of International Entrepreneurship, Springer, vol. 22(2), pages 141-163, June.
    4. Rodepeter, Elisa & Gschnaidtner, Christoph & Hottenrott, Hanna, 2023. "Big data and start-up performance," ZEW Discussion Papers 23-061, ZEW - Leibniz Centre for European Economic Research.
    5. Victor Aguirregabiria & Jihye Jeon, 2020. "Firms’ Beliefs and Learning: Models, Identification, and Empirical Evidence," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 56(2), pages 203-235, March.
    6. Huang, Qiongyu & Xu, Chuhong & Xue, Xiaolong & Zhu, Hui, 2023. "Can digital innovation improve firm performance: Evidence from digital patents of Chinese listed firms," International Review of Financial Analysis, Elsevier, vol. 89(C).
    7. Yiquan Gu & Leonardo Madio & Carlo Reggiani, 2021. "Data Brokers Co-Opetition," Working Papers 202101, University of Liverpool, Department of Economics.
    8. Bo Cowgill, 2019. "Bias and Productivity in Humans and Machines," Upjohn Working Papers 19-309, W.E. Upjohn Institute for Employment Research.
    9. Schaefer, Maximilian & Sapi, Geza & Lorincz, Szabolcs, 2018. "The effect of big data on recommendation quality: The example of internet search," DICE Discussion Papers 284, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    10. Catherine Tucker, 2019. "Digital Data, Platforms and the Usual [Antitrust] Suspects: Network Effects, Switching Costs, Essential Facility," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 54(4), pages 683-694, June.
    11. Wang, Jiaxin & Zhao, Mu & Huang, Xiang & Song, Zilong & Sun, Di, 2024. "Supply chain diffusion mechanisms for AI applications: A perspective on audit pricing," International Review of Financial Analysis, Elsevier, vol. 93(C).
    12. Helmut Wasserbacher & Martin Spindler, 2021. "Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls," Papers 2107.04851, arXiv.org.
    13. Federico Etro, 2020. "Product Selection in Online Marketplaces," Working Papers - Economics wp2020_20.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    14. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
    15. Guy Aridor & Yeon-Koo Che & Tobias Salz, 2020. "The Effect of Privacy Regulation on the Data Industry: Empirical Evidence from GDPR," NBER Working Papers 26900, National Bureau of Economic Research, Inc.
    16. Marie-Pierre Dargnies & Rustamdjan Hakimov & Dorothee Kübler, 2023. "Aversion to hiring algorithms: Transparency, gender profiling, and self-confidence," Post-Print hal-04413060, HAL.
    17. Anderton, Robert & Jarvis, Valerie & Labhard, Vincent & Morgan, Julian & Petroulakis, Filippos & Vivian, Lara, 2020. "Virtually everywhere? Digitalisation and the euro area and EU economies," Occasional Paper Series 244, European Central Bank.
    18. Charles B. Weinberg & Cord Otten & Barak Orbach & Jordi McKenzie & Ricard Gil & Darlene C. Chisholm & Suman Basuroy, 2021. "Technological change and managerial challenges in the movie theater industry," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 45(2), pages 239-262, June.
    19. Graef, Inge & Prüfer, Jens, 2021. "Governance of Data Sharing : a Law & Economics Proposal," Discussion Paper 2021-004, Tilburg University, Center for Economic Research.
    20. Charles I. Jones & Christopher Tonetti, 2019. "Nonrivalry and the Economics of Data," NBER Working Papers 26260, National Bureau of Economic Research, Inc.
    21. Ehsan Valavi & Joel Hestness & Newsha Ardalani & Marco Iansiti, 2022. "Time and the Value of Data," Papers 2203.09118, arXiv.org.
    22. Norbäck, Pehr-Johan & Persson, Lars, 2023. "Why Big Data Can Make Creative Destruction More Creative – But Less Destructive," Working Paper Series 1454, Research Institute of Industrial Economics.
    23. Koski, Heli & Kässi, Otto & Braesemann, Fabian, 2020. "Killers on the Road of Emerging Start-ups – Implications for Market Entry and Venture Capital Financing," ETLA Working Papers 81, The Research Institute of the Finnish Economy.
    24. Dirk Bergemann & Marco Ottaviani, 2021. "Information Markets and Nonmarkets," Cowles Foundation Discussion Papers 2296, Cowles Foundation for Research in Economics, Yale University.
    25. Shan Huang & Michael Allan Ribers & Hannes Ullrich, 2021. "The Value of Data for Prediction Policy Problems: Evidence from Antibiotic Prescribing," Discussion Papers of DIW Berlin 1939, DIW Berlin, German Institute for Economic Research.
    26. Carl-Christian Groh, 2024. "Big Data and Inequality," CRC TR 224 Discussion Paper Series crctr224_2024_555, University of Bonn and University of Mannheim, Germany.
    27. Erik Brynjolfsson & Wang Jin & Kristina McElheran, 2021. "The power of prediction: predictive analytics, workplace complements, and business performance," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 56(4), pages 217-239, October.
    28. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
    29. MARTENS Bertin, 2020. "An economic perspective on data and platform market power," JRC Working Papers on Digital Economy 2020-09, Joint Research Centre.
    30. Fossen, Frank M. & McLemore, Trevor & Sorgner, Alina, 2024. "Artificial Intelligence and Entrepreneurship," IZA Discussion Papers 17055, Institute of Labor Economics (IZA).
    31. Larson, William D. & Sinclair, Tara M., 2022. "Nowcasting unemployment insurance claims in the time of COVID-19," International Journal of Forecasting, Elsevier, vol. 38(2), pages 635-647.
    32. Guy Aridor & Yishay Mansour & Aleksandrs Slivkins & Zhiwei Steven Wu, 2020. "Competing Bandits: The Perils of Exploration Under Competition," Papers 2007.10144, arXiv.org, revised Oct 2024.
    33. Kesler, Reinhold & Kummer, Michael E. & Schulte, Patrick, 2019. "Competition and privacy in online markets: Evidence from the mobile app industry," ZEW Discussion Papers 19-064, ZEW - Leibniz Centre for European Economic Research.
    34. Simon Martin & Wolfgang Benedikt Schmal, 2021. "Collusive Compensation Schemes Aided by Algorithms," CESifo Working Paper Series 9481, CESifo.
    35. Calvano, Emilio & Polo, Michele, 2020. "Market Power, Competition and Innovation in digital markets: a survey," CEPR Discussion Papers 14314, C.E.P.R. Discussion Papers.
    36. Pehr-Johan Norbäck & Lars Persson, 2024. "Why generative AI can make creative destruction more creative but less destructive," Small Business Economics, Springer, vol. 63(1), pages 349-377, June.
    37. Li, Yuetong & Wang, Xinyi & Zheng, Xiaojia, 2024. "Data assets and corporate sustainable development: evidence from ESG in China," Pacific-Basin Finance Journal, Elsevier, vol. 85(C).
    38. Yang, Gangqiang & Nie, Yiming & Li, Honggui & Wang, Haisen, 2023. "Digital transformation and low-carbon technology innovation in manufacturing firms: The mediating role of dynamic capabilities," International Journal of Production Economics, Elsevier, vol. 263(C).
    39. Schaefer, Maximilian & Sapi, Geza, 2023. "Complementarities in learning from data: Insights from general search," Information Economics and Policy, Elsevier, vol. 65(C).
    40. Maximilian Schäfer & Geza Sapi, 2020. "Learning from Data and Network Effects: The Example of Internet Search," Discussion Papers of DIW Berlin 1894, DIW Berlin, German Institute for Economic Research.

  51. Victor Chernozhukov & Alfred Galichon & Marc Henry & Brendan Pass, 2018. "Single Market Nonparametric Identification of Multi-Attribute Hedonic Equilibrium Models," SciencePo Working papers Main hal-01169655, HAL.

    Cited by:

    1. Dmitry Arkhangelsky, 2019. "Dealing with a Technological Bias: The Difference-in-Difference Approach," Working Papers wp2019_1903, CEMFI.
    2. Odran Bonnet & Alfred Galichon & Yu-Wei Hsieh & Keith O’Hara & Matt Shum, 2022. "Yogurts Choose Consumers? Estimation of Random-Utility Models via Two-Sided Matching," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(6), pages 3085-3114.
    3. Pierre-André Chiappori & Bernard Salanié, 2016. "The Econometrics of Matching Models," Journal of Economic Literature, American Economic Association, vol. 54(3), pages 832-861, September.
    4. Florian Gunsilius, 2018. "Point-identification in multivariate nonseparable triangular models," Papers 1806.09680, arXiv.org.
    5. Dizdar, Deniz & Moldovanu, Benny, 2016. "On the importance of uniform sharing rules for efficient matching," Journal of Economic Theory, Elsevier, vol. 165(C), pages 106-123.

  52. Victor Chernozhukov & Wolfgang K. Hardle & Chen Huang & Weining Wang, 2018. "LASSO-Driven Inference in Time and Space," Papers 1806.05081, arXiv.org, revised May 2020.

    Cited by:

    1. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    2. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023. "Granger Causality Testing in High-Dimensional VARs: A Post-Double-Selection Procedure," Journal of Financial Econometrics, Oxford University Press, vol. 21(3), pages 915-958.
    3. Fan, Qingliang & Zhong, Wei, 2018. "Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective," IRTG 1792 Discussion Papers 2018-052, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    4. Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl & Klochkov, Yegor, 2022. "SONIC: SOcial Network analysis with Influencers and Communities," Journal of Econometrics, Elsevier, vol. 228(2), pages 177-220.
    5. Michael Kostmann & Wolfgang K. Härdle, 2019. "Forecasting in Blockchain-Based Local Energy Markets," Energies, MDPI, vol. 12(14), pages 1-27, July.
    6. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Zbonakova, Lenka & Pio Monti, Ricardo & Härdle, Wolfgang Karl, 2018. "Towards the interpretation of time-varying regularization parameters in streaming penalized regression models," IRTG 1792 Discussion Papers 2018-059, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    8. Davide Viviano, 2019. "Policy Targeting under Network Interference," Papers 1906.10258, arXiv.org, revised Apr 2024.
    9. Zhong, Wei & Liu, Xi & Ma, Shuangge, 2018. "Variable selection and direction estimation for single-index models via DC-TGDR method," IRTG 1792 Discussion Papers 2018-050, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    10. Chen, C. Y-H. & Härdle, W. K. & Klochkov, Y., 2019. "Influencers and Communities in Social Networks," Cambridge Working Papers in Economics 1998, Faculty of Economics, University of Cambridge.
    11. Guo, Li & Tao, Yubo & Härdle, Wolfgang Karl, 2018. "Understanding Latent Group Structure of Cryptocurrencies Market: A Dynamic Network Perspective," IRTG 1792 Discussion Papers 2018-032, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    12. Munday, Tim & Brookes, James, 2021. "Mark my words: the transmission of central bank communication to the general public via the print media," Bank of England working papers 944, Bank of England.
    13. Packham, Natalie & Woebbeking, Fabian, 2018. "A factor-model approach for correlation scenarios and correlation stress-testing," IRTG 1792 Discussion Papers 2018-034, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    14. Xiaojia Bao & Qingliang Fan, 2020. "The impact of temperature on gaming productivity: evidence from online games," Empirical Economics, Springer, vol. 58(2), pages 835-867, February.
    15. Smeekes, Stephan & Wijler, Etienne, 2021. "An automated approach towards sparse single-equation cointegration modelling," Journal of Econometrics, Elsevier, vol. 221(1), pages 247-276.
    16. Packham, Natalie & Kalkbrener, Michael & Overbeck, Ludger, 2018. "Default probabilities and default correlations under stress," IRTG 1792 Discussion Papers 2018-037, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    17. Kuczmaszewska, Anna & Yan, Ji Gao, 2018. "On complete convergence in Marcinkiewicz-Zygmund type SLLN for random variables," IRTG 1792 Discussion Papers 2018-041, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    18. Chen, Haiqiang & Li, Yingxing & Lin, Ming & Zhu, Yanli, 2018. "A Regime Shift Model with Nonparametric Switching Mechanism," IRTG 1792 Discussion Papers 2018-048, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    19. Yatracos, Yannis G., 2018. "Residual'S Influence Index (Rinfin), Bad Leverage And Unmasking In High Dimensional L2-Regression," IRTG 1792 Discussion Papers 2018-060, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    20. Zbonakova, Lenka & Li, Xinjue & Härdle, Wolfgang Karl, 2018. "Penalized Adaptive Forecasting with Large Information Sets and Structural Changes," IRTG 1792 Discussion Papers 2018-039, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    21. Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl & Klochkov, Yegor, 2019. "SONIC: SOcial Network with Influencers and Communities," IRTG 1792 Discussion Papers 2019-025, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    22. Packham, Natalie, 2018. "Optimal contracts under competition when uncertainty from adverse selection and moral hazard are present," IRTG 1792 Discussion Papers 2018-033, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    23. Cai, Zongwu & Fang, Ying & Lin, Ming & Su, Jia, 2018. "Inferences for a Partially Varying Coefficient Model With Endogenous Regressors," IRTG 1792 Discussion Papers 2018-047, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    24. Wang, Honglin & Yu, Fan & Zhou, Yinggang, 2018. "Property Investment and Rental Rate under Housing Price Uncertainty: A Real Options Approach," IRTG 1792 Discussion Papers 2018-051, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    25. Yan, Ji Gao, 2018. "Complete Convergence and Complete Moment Convergence for Maximal Weighted Sums of Extended Negatively Dependent Random Variables," IRTG 1792 Discussion Papers 2018-040, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    26. Kalkbrener, Michael & Packham, Natalie, 2018. "Correlation Under Stress In Normal Variance Mixture Models," IRTG 1792 Discussion Papers 2018-035, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    27. Chiu, Hsin-Yu & Chiang, Mi-Hsiu & Kuo, Wei-Yu, 2018. "Predicative Ability of Similarity-based Futures Trading Strategies," IRTG 1792 Discussion Papers 2018-045, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    28. Guo, Shaojun & Li, Dong & Li, Muyi, 2018. "Strict Stationarity Testing and GLAD Estimation of Double Autoregressive Models," IRTG 1792 Discussion Papers 2018-049, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    29. Mihoci, Andrija & Althof, Michael & Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl, 2019. "FRM Financial Risk Meter," IRTG 1792 Discussion Papers 2019-021, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    30. Dovì, Max-Sebastian & Koester, Gerrit & Nickel, Christiane, 2021. "Addressing the endogeneity of slack in Phillips Curves," Working Paper Series 2619, European Central Bank.
    31. Panxu Yuan & Xiao Guo, 2022. "High-dimensional inference for linear model with correlated errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(1), pages 21-52, January.
    32. Koziuk, Andzhey & Spokoiny, Vladimir, 2018. "Toolbox: Gaussian comparison on Eucledian balls," IRTG 1792 Discussion Papers 2018-028, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

  53. Victor Chernozhukov & Whitney K Newey & Rahul Singh, 2018. "Automatic Debiased Machine Learning of Causal and Structural Effects," Papers 1809.05224, arXiv.org, revised Oct 2022.

    Cited by:

    1. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    2. TELLO, Mario D., 2024. "Inversión Pública En Infraestructura Y Crecimiento Regional En Perú, 2005-2020: Un Análisis Basado En Técnicas De Aprendizaje Automático Causal," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 24(2), pages 195-222.
    3. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2020. "Unconditional Quantile Regression with High Dimensional Data," Papers 2007.13659, arXiv.org, revised Feb 2022.
    4. Helena Chuliá & Sabuhi Khalili & Jorge M. Uribe, 2024. "Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI," IREA Working Papers 202402, University of Barcelona, Research Institute of Applied Economics, revised Feb 2024.
    5. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
    6. Soren Blomquist & Anil Kumar & Che-Yuan Liang & Whitney K. Newey, 2022. "Nonlinear Budget Set Regressions for the Random Utility Model," Working Papers 2219, Federal Reserve Bank of Dallas.
    7. Paul Clarke & Annalivia Polselli, 2023. "Double Machine Learning for Static Panel Models with Fixed Effects," Papers 2312.08174, arXiv.org, revised Sep 2024.
    8. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
    9. Manu Navjeevan, 2023. "An Identification and Dimensionality Robust Test for Instrumental Variables Models," Papers 2311.14892, arXiv.org, revised Dec 2024.
    10. Juan Carlos Escanciano & Telmo P'erez-Izquierdo, 2023. "Automatic Locally Robust Estimation with Generated Regressors," Papers 2301.10643, arXiv.org, revised Nov 2023.
    11. Gyungbae Park, 2024. "Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions," Papers 2403.15934, arXiv.org.
    12. Newham, Melissa & Valente, Marica, 2024. "The cost of influence: How gifts to physicians shape prescriptions and drug costs," Journal of Health Economics, Elsevier, vol. 95(C).
    13. Paul Goldsmith-Pinkham & Peter Hull & Michal Kolesár, 2024. "Contamination Bias in Linear Regressions," American Economic Review, American Economic Association, vol. 114(12), pages 4015-4051, December.
    14. Jonathan Fuhr & Dominik Papies, 2024. "Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions," Papers 2409.01266, arXiv.org.
    15. Jikai Jin & Vasilis Syrgkanis, 2024. "Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation," Papers 2402.14264, arXiv.org, revised Mar 2024.
    16. Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022. "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers 2211.07903, arXiv.org.
    17. Manu Navjeevan & Rodrigo Pinto & Andres Santos, 2023. "Identification and Estimation in a Class of Potential Outcomes Models," Papers 2310.05311, arXiv.org.
    18. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    19. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    20. Amandeep Singh & Ye Liu & Hema Yoganarasimhan, 2023. "Choice Models and Permutation Invariance: Demand Estimation in Differentiated Products Markets," Papers 2307.07090, arXiv.org, revised Feb 2024.
    21. Zhengyu Zhang & Zequn Jin & Lihua Lin, 2024. "Identification and inference of outcome conditioned partial effects of general interventions," Papers 2407.16950, arXiv.org.
    22. Geonwoo Kim & Suyong Song, 2024. "Double/Debiased CoCoLASSO of Treatment Effects with Mismeasured High-Dimensional Control Variables," Papers 2408.14671, arXiv.org.
    23. Victor Chernozhukov & Whitney K Newey & Rahul Singh, 2018. "Automatic Debiased Machine Learning of Causal and Structural Effects," Papers 1809.05224, arXiv.org, revised Oct 2022.
    24. Zhang, Jeffrey & Li, Wei & Miao, Wang & Tchetgen Tchetgen, Eric, 2023. "Proximal causal inference without uniqueness assumptions," Statistics & Probability Letters, Elsevier, vol. 198(C).
    25. Liu, Lin & Mukherjee, Rajarshi & Robins, James M., 2024. "Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators," Journal of Econometrics, Elsevier, vol. 240(2).
    26. Zhan Gao & Ji Hyung Lee & Ziwei Mei & Zhentao Shi, 2024. "Econometric Inference for High Dimensional Predictive Regressions," Papers 2409.10030, arXiv.org, revised Nov 2024.
    27. Esfandiar Maasoumi & Jianqiu Wang & Zhuo Wang & Ke Wu, 2024. "Identifying factors via automatic debiased machine learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 438-461, April.
    28. Paul Goldsmith-Pinkham & Peter Hull & Michal Kolesár, 2021. "On Estimating Multiple Treatment Effects with Regression," Working Papers 2021-41, Princeton University. Economics Department..

  54. Victor Chernozhukov & Whitney K. Newey & James Robins, 2018. "Double/de-biased machine learning using regularized Riesz representers," CeMMAP working papers CWP15/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    3. Adel Daoud, 2021. "The International Monetary Funds intervention in education systems and its impact on childrens chances of completing school," Papers 2201.00013, arXiv.org.
    4. Daoud, Adel & Johansson, Fredrik, 2019. "Estimating Treatment Heterogeneity of International Monetary Fund Programs on Child Poverty with Generalized Random Forest," SocArXiv awfjt, Center for Open Science.
    5. Yiyan Huang & Cheuk Hang Leung & Xing Yan & Qi Wu & Nanbo Peng & Dongdong Wang & Zhixiang Huang, 2020. "The Causal Learning of Retail Delinquency," Papers 2012.09448, arXiv.org.
    6. Victor Chernozhukov & Jerry A. Hausman & Whitney K. Newey, 2019. "Demand Analysis with Many Prices," NBER Working Papers 26424, National Bureau of Economic Research, Inc.
    7. Daoud, Adel, 2021. "The International Monetary Fund’s intervention in education systems and its impact on children’s chances of completing school," SocArXiv kbc34, Center for Open Science.
    8. Jann Spiess & Vasilis Syrgkanis & Victor Yaneng Wang, 2021. "Finding Subgroups with Significant Treatment Effects," Papers 2103.07066, arXiv.org, revised Dec 2023.

  55. Victor Chernozhukov & Alfred Galichon & Marc Hallin & Marc Henry, 2017. "Monge-Kantorovich Depth, Quantiles, Ranks, and Signs," SciencePo Working papers Main hal-03391975, HAL.

    Cited by:

    1. Marc Hallin & Daniel Hlubinka & Šárka Hudecová, 2023. "Efficient Fully Distribution-Free Center-Outward Rank Tests for Multiple-Output Regression and MANOVA," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(543), pages 1923-1939, July.
    2. Marcel Klatt & Axel Munk & Yoav Zemel, 2022. "Limit laws for empirical optimal solutions in random linear programs," Annals of Operations Research, Springer, vol. 315(1), pages 251-278, August.
    3. Dmitry Arkhangelsky, 2019. "Dealing with a Technological Bias: The Difference-in-Difference Approach," Working Papers wp2019_1903, CEMFI.
    4. Gunsilius, Florian F., 2023. "A condition for the identification of multivariate models with binary instruments," Journal of Econometrics, Elsevier, vol. 235(1), pages 220-238.
    5. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," SciencePo Working papers Main hal-03936221, HAL.
    6. Hamel, Andreas H. & Kostner, Daniel, 2018. "Cone distribution functions and quantiles for multivariate random variables," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 97-113.
    7. Marc Hallin & Hongjian Shi & Mathias Drton & Fang Han, 2021. "Center-Outward Sign- and Rank-Based Quadrant, Spearman, and Kendall Tests for Multivariate Independence," Working Papers ECARES 2021-27, ULB -- Universite Libre de Bruxelles.
    8. Manuel Arellano & Stéphane Bonhomme, 2019. "Recovering Latent Variables by Matching," Working Papers wp2019_1914, CEMFI.
    9. María Edo & Walter Sosa Escudero & Marcela Svarc, 2021. "A multidimensional approach to measuring the middle class," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(1), pages 139-162, March.
    10. Lixiong Li & Marc Henry, 2022. "Finite Sample Inference in Incomplete Models," Papers 2204.00473, arXiv.org, revised Apr 2024.
    11. Marc Hallin, 2021. "Measure Transportation and Statistical Decision Theory," Working Papers ECARES 2021-04, ULB -- Universite Libre de Bruxelles.
    12. Hudecová, Šárka & Šiman, Miroslav, 2022. "Multivariate ranks based on randomized lift-interdirections," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    13. Yanqin Fan & Marc Henry, 2020. "Vector copulas," Papers 2009.06558, arXiv.org, revised Apr 2021.
    14. Marc Hallin & Davide La Vecchia & Hang Liu, 2020. "Rank-Based Testing for Semiparametric VAR Models: a measure transportation approach," Working Papers ECARES 2020-47, ULB -- Universite Libre de Bruxelles.
    15. Florian Gunsilius & Susanne Schennach, 2023. "Independent Nonlinear Component Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(542), pages 1305-1318, April.
    16. M. Hallin & D. La Vecchia & H. Liu, 2022. "Center-Outward R-Estimation for Semiparametric VARMA Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(538), pages 925-938, April.
    17. Kotík, Lukáš & Hlubinka, Daniel, 2017. "A weighted localization of halfspace depth and its properties," Journal of Multivariate Analysis, Elsevier, vol. 157(C), pages 53-69.
    18. Alberto González-Sanz & Marc Hallin & Bodhisattva Sen, 2023. "Monotone Measure-Preserving Maps in Hilbert Spaces: Existence, Uniqueness, and Stability," Working Papers ECARES 2023-10, ULB -- Universite Libre de Bruxelles.
    19. Olivier Paul Faugeras & Ludger Rüschendorf, 2021. "Functional, randomized and smoothed multivariate quantile regions," Post-Print hal-03352330, HAL.
    20. Marc Hallin, 2018. "From Mahalanobis to Bregman via Monge and Kantorovich towards a “General Generalised Distance”," Working Papers ECARES 2018-12, ULB -- Universite Libre de Bruxelles.
    21. Fan, Yanqin & Henry, Marc, 2023. "Vector copulas," Journal of Econometrics, Elsevier, vol. 234(1), pages 128-150.
    22. Marc Hallin & Daniel Hlubinka & Sarka Hudecova, 2020. "Fully Distribution-free Center-outward Rank Tests for Multiple-output Regression and Manova," Working Papers ECARES 2020-32, ULB -- Universite Libre de Bruxelles.
    23. Jan Bierlant & Sven Buitendag & Eustasio Del Barrio & Marc Hallin, 2019. "Center-Outward Quantiles And The Measurement Of Multivariate Risk," Working Papers ECARES 2019-30, ULB -- Universite Libre de Bruxelles.
    24. Petra Laketa & Stanislav Nagy, 2022. "Halfspace depth for general measures: the ray basis theorem and its consequences," Statistical Papers, Springer, vol. 63(3), pages 849-883, June.
    25. Davy Paindaveine & Germain Van Bever, 2017. "Halfspace Depths for Scatter, Concentration and Shape Matrices," Working Papers ECARES ECARES 2017-19, ULB -- Universite Libre de Bruxelles.
    26. Marc Hallin & Hang Liu, 2022. "Center-outward Rank- and Sign-based VARMA Portmanteau Tests," Working Papers ECARES 2022-27, ULB -- Universite Libre de Bruxelles.
    27. Florian Gunsilius, 2018. "Point-identification in multivariate nonseparable triangular models," Papers 1806.09680, arXiv.org.
    28. Marc Hallin, 2018. "From Mahalanobis to Bregman via Monge and Kantorovich," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 135-146, December.
    29. Yanqin Fan & Marc Henry & Brendan Pass & Jorge A. Rivero, 2022. "Lorenz map, inequality ordering and curves based on multidimensional rearrangements," Papers 2203.09000, arXiv.org, revised Apr 2024.
    30. Faugeras, Olivier P. & Rüschendorf, Ludger, 2021. "Functional, randomized and smoothed multivariate quantile regions," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    31. Hongjian Shi & Mathias Drton & Marc Hallin & Fang Han, 2023. "Semiparametrically Efficient Tests of Multivariate Independence Using Center-Outward Quadrant, Spearman, and Kendall Statistics," Working Papers ECARES 2023-03, ULB -- Universite Libre de Bruxelles.
    32. Marc Hallin & H Lui & Thomas Verdebout, 2022. "Nonparametric Measure-transportation-based Methods for Directional Data," Working Papers ECARES 2022-18, ULB -- Universite Libre de Bruxelles.
    33. Marc Hallin & Gilles Mordant, 2021. "On the Finite-Sample Performance of Measure Transportation-Based Multivariate Rank Tests," Working Papers ECARES 2021-24, ULB -- Universite Libre de Bruxelles.
    34. Hongjian Shi & Marc Hallin & Mathias Drton & Fang Han, 2020. "Rate-Optimality of Consistent Distribution-Free Tests of Independence Based on Center-Outward Ranks and Signs," Working Papers ECARES 2020-23, ULB -- Universite Libre de Bruxelles.
    35. Eustasio Del Barrio & Juan Cuesta Albertos & Marc Hallin & Carlos Matran, 2018. "Smooth Cyclically Monotone Interpolation and Empirical Center-Outward Distribution Functions," Working Papers ECARES 2018-15, ULB -- Universite Libre de Bruxelles.
    36. Eustasio del Barrio & Alberto González-Sanz & Marc Hallin, 2022. "Nonparametric Multiple-Output Center-Outward Quantile Regression," Working Papers ECARES 2022-10, ULB -- Universite Libre de Bruxelles.
    37. Alfred Galichon & Bernard Salani'e, 2021. "Cupid's Invisible Hand: Social Surplus and Identification in Matching Models," Papers 2106.02371, arXiv.org, revised Jan 2023.
    38. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Working Papers hal-03936221, HAL.

  56. Victor Chernozhukov & Alfred Galichon & Marc Henry & Brendan Pass, 2017. "Identification of hedonic equilibrium and nonseparable simultaneous equations," Papers 1709.09570, arXiv.org, revised Feb 2021.

    Cited by:

    1. Gunsilius, Florian F., 2023. "A condition for the identification of multivariate models with binary instruments," Journal of Econometrics, Elsevier, vol. 235(1), pages 220-238.
    2. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," SciencePo Working papers Main hal-03936221, HAL.
    3. Tongseok Lim, 2023. "Replication of financial derivatives under extreme market models given marginals," Papers 2307.00807, arXiv.org.
    4. Victor H. Aguiar & Nail Kashaev & Roy Allen, 2022. "Prices, Profits, Proxies, and Production," University of Western Ontario, Departmental Research Report Series 20226, University of Western Ontario, Department of Economics.
    5. William Torous & Florian Gunsilius & Philippe Rigollet, 2021. "An Optimal Transport Approach to Estimating Causal Effects via Nonlinear Difference-in-Differences," Papers 2108.05858, arXiv.org, revised Mar 2024.
    6. Bishop, Kelly C. & Timmins, Christopher, 2019. "Estimating the marginal willingness to pay function without instrumental variables," Journal of Urban Economics, Elsevier, vol. 109(C), pages 66-83.
    7. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Working Papers hal-03936221, HAL.
    8. Alfred Galichon, 2021. "The unreasonable effectiveness of optimal transport in economics," Papers 2107.04700, arXiv.org.

  57. Victor Chernozhukov & Mert Demirer & Esther Duflo & Ivan Fernandez-Val, 2017. "Generic machine learning inference on heterogenous treatment effects in randomized experiments," CeMMAP working papers 61/17, Institute for Fiscal Studies.

    Cited by:

    1. Daniel Lewis & Davide Melcangi & Laura Pilossoph, 2024. "Latent Heterogeneity in the Marginal Propensity to Consume," NBER Working Papers 32523, National Bureau of Economic Research, Inc.
    2. Knaus, Michael C., 2020. "Double Machine Learning Based Program Evaluation under Unconfoundedness," IZA Discussion Papers 13051, Institute of Labor Economics (IZA).
    3. Alex Armand & Britta Augsburg & Antonella Bancalari, 2021. "Coordination and the poor maintenance trap: an experiment on public infrastructure in India," IFS Working Papers W21/16, Institute for Fiscal Studies.
    4. Vira Semenova, 2020. "Generalized Lee Bounds," Papers 2008.12720, arXiv.org, revised Feb 2023.
    5. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2021. "Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach," Working Paper Series 2614, European Central Bank.
    6. Paul B. Ellickson & Wreetabrata Kar & James C. Reeder, 2023. "Estimating Marketing Component Effects: Double Machine Learning from Targeted Digital Promotions," Marketing Science, INFORMS, vol. 42(4), pages 704-728, July.
    7. Armand, Alex & Augsburg, Britta & Bancalari, Antonella & Ghatak, Maitreesh, 2023. "Public Service Delivery, Exclusion and Externalities: Theory and Experimental Evidence from India," CEPR Discussion Papers 18636, C.E.P.R. Discussion Papers.
    8. Riccardo Di Francesco, 2024. "Aggregation Trees," Papers 2410.11408, arXiv.org.
    9. Stephen Coussens & Jann Spiess, 2021. "Improving Inference from Simple Instruments through Compliance Estimation," Papers 2108.03726, arXiv.org.
    10. O'Neill, E. & Weeks, M., 2018. "Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes," Cambridge Working Papers in Economics 1865, Faculty of Economics, University of Cambridge.
    11. Matias D. Cattaneo & Max H. Farrell & Yingjie Feng, 2018. "Large Sample Properties of Partitioning-Based Series Estimators," Papers 1804.04916, arXiv.org, revised Jun 2019.

  58. Victor Chernozhukov & Iv'an Fern'andez-Val & Whitney Newey & Sami Stouli & Francis Vella, 2017. "Semiparametric Estimation of Structural Functions in Nonseparable Triangular Models," Papers 1711.02184, arXiv.org, revised Oct 2019.

    Cited by:

    1. Romuald Meango & Esther Mirjam Girsberger, 2023. "Identification of Ex ante Returns Using Elicited Choice Probabilities: an Application to Preferences for Public-sector Jobs," Papers 2303.03009, arXiv.org, revised Jun 2024.
    2. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey & Sami Stouli & Francis Vella, 2017. "Semiparametric estimation of structural functions in nonseparable triangular models," CeMMAP working papers CWP48/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Bruneel-Zupanc, Christophe Alain, 2021. "Discrete-Continuous Dynamic Choice Models: Identification and Conditional Choice Probability Estimation," TSE Working Papers 21-1185, Toulouse School of Economics (TSE).
    4. Fernandez-Val , Ivan & van Vuuren, Aico & Vella, Francis, 2018. "Nonseparable Sample Selection Models with Censored Selection Rules," Working Papers in Economics 716, University of Gothenburg, Department of Economics.
    5. Whitney K. Newey & Sami Stouli, 2018. "Heterogenous coefficients, discrete instruments, and identification of treatment effects," CeMMAP working papers CWP66/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Nir Billfeld & Moshe Kim, 2024. "Context-dependent Causality (the Non-Nonotonic Case)," Papers 2404.05021, arXiv.org.
    7. Fernández-Val, Iván & van Vuuren, Aico & Vella, Francis & Peracchi, Franco, 2022. "Selection and the Distribution of Female Hourly Wages in the U.S," IZA Discussion Papers 15028, Institute of Labor Economics (IZA).
    8. Whitney Newey & Sami Stouli, 2018. "Control Variables, Discrete Instruments, and Identification of Structural Functions," Bristol Economics Discussion Papers 18/702, School of Economics, University of Bristol, UK.
    9. Romauld Méango, 2023. "Identification of ex ante returns using elicited choice probabilities," Economics Series Working Papers 1007, University of Oxford, Department of Economics.
    10. Jayeeta Bhattacharya, 2020. "Quantile regression with generated dependent variable and covariates," Papers 2012.13614, arXiv.org.
    11. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    12. Tadao Hoshino, 2021. "Estimating a Continuous Treatment Model with Spillovers: A Control Function Approach," Papers 2112.15114, arXiv.org, revised Jan 2023.
    13. Nagasawa, Kenichi, 2020. "Identification and Estimation of Group-Level Partial Effects," The Warwick Economics Research Paper Series (TWERPS) 1243, University of Warwick, Department of Economics.
    14. Iván Fernández‐Val & Aico van Vuuren & Francis Vella & Franco Peracchi, 2023. "Selection and the distribution of female real hourly wages in the United States," Quantitative Economics, Econometric Society, vol. 14(2), pages 571-607, May.
    15. Christian Gische & Manuel C. Voelkle, 2022. "Beyond the Mean: A Flexible Framework for Studying Causal Effects Using Linear Models," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 868-901, September.
    16. Jonas Meier, 2020. "Multivariate Distribution Regression," Diskussionsschriften dp2023, Universitaet Bern, Departement Volkswirtschaft.
    17. Richard Spady & Sami Stouli, 2020. "Gaussian Transforms Modeling and the Estimation of Distributional Regression Functions," Papers 2011.06416, arXiv.org.

  59. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iv'an Fern'andez-Val, 2017. "Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," Papers 1712.04802, arXiv.org, revised Oct 2023.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Christiansen, T. & Weeks, M., 2020. "Distributional Aspects of Microcredit Expansions," Cambridge Working Papers in Economics 20100, Faculty of Economics, University of Cambridge.
    3. Michael Vlassopoulos & Abu Siddique & Tabassum Rahman & Debayan Pakrashi & Asad Islam & Firoz Ahmed, 2024. "Improving Women's Mental Health during a Pandemic," American Economic Journal: Applied Economics, American Economic Association, vol. 16(2), pages 422-455, April.
    4. Dar, Manzoor H. & de Janvry, Alain & Emerick, Kyle & Kelley, Erin M. & Sadoulet, Elisabeth, 2019. "Endogenous Information Sharing and the Gains from Using Network Information to Maximize Technology Adoption," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt8qx7m4zq, Department of Agricultural & Resource Economics, UC Berkeley.
    5. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
    6. Kayo MURAKAMI & Hideki SHIMADA & Yoshiaki USHIFUSA & Takanori IDA, 2020. "Heterogeneous Treatment Effects of Nudge and Rebate:Causal Machine Learning in a Field Experiment on Electricity Conservation," Discussion papers e-20-003, Graduate School of Economics , Kyoto University.
    7. Du, Tianyu & Kanodia, Ayush & Brunborg, Herman & Vafa, Keyon & Athey, Susan, 2024. "Labor-LLM: Language-Based Occupational Representations with Large Language Models," Research Papers 4188, Stanford University, Graduate School of Business.
    8. Marianne Bertrand & Bruno Crépon & Alicia Marguerie & Patrick Premand, 2021. "Do Workfare Programs Live Up to Their Promises? Experimental Evidence from Cote D’Ivoire," NBER Working Papers 28664, National Bureau of Economic Research, Inc.
    9. Strittmatter, Anthony, 2019. "What is the Value Added by using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203499, Verein für Socialpolitik / German Economic Association.
    10. Islam, Asadul & Mozumder, Tanvir Ahmed & Rahman, Tabassum & Shatil, Tanvir & Siddique, Abu, 2024. "Forced Displacement, Mental Health, and Child Development: Evidence from the Rohingya Refugees," IZA Discussion Papers 16961, Institute of Labor Economics (IZA).
    11. Hoong, Ruru, 2021. "Self control and smartphone use: An experimental study of soft commitment devices," European Economic Review, Elsevier, vol. 140(C).
    12. Patrick Rehill, 2024. "How do applied researchers use the Causal Forest? A methodological review of a method," Papers 2404.13356, arXiv.org, revised Dec 2024.
    13. Chaisemartin, Clement de & Navarrete, Nicolas, 2019. "The direct and spillover effects of a mental health program for disruptive students," CAGE Online Working Paper Series 401, Competitive Advantage in the Global Economy (CAGE).
    14. Pramod Kumar Sur, 2021. "Understanding Vaccine Hesitancy: Empirical Evidence from India," Papers 2103.02909, arXiv.org, revised Feb 2023.
    15. Bruno Fava, 2024. "Predicting the Distribution of Treatment Effects: A Covariate-Adjustment Approach," Papers 2407.14635, arXiv.org, revised Oct 2024.

  60. Alexandre Belloni & Christian Hansen & Whitney Newey, 2017. "Simultaneous Confidence Intervals for High-dimensional Linear Models with Many Endogenous Variables," Papers 1712.08102, arXiv.org, revised Aug 2019.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Victor Chernozhukov & Chen Huang & Weining Wang, 2021. "Uniform Inference on High-dimensional Spatial Panel Networks," Papers 2105.07424, arXiv.org, revised Sep 2023.
    3. Caner, Mehmet, 2023. "Generalized linear models with structured sparsity estimators," Journal of Econometrics, Elsevier, vol. 236(2).
    4. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2020. "Ill-posed estimation in high-dimensional models with instrumental variables," Journal of Econometrics, Elsevier, vol. 219(1), pages 171-200.
    5. Baris Ata & Alexandre Belloni & Ozan Candogan, 2018. "Latent Agents in Networks: Estimation and Targeting," Papers 1808.04878, arXiv.org, revised Jan 2022.

  61. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul & Mathieu Rosenbaum & Alexandre B. Tsybakov, 2017. "Pivotal Estimation Via Self-Normalization for High-Dimensional Linear Models with Errors in Variables," Working Papers 2017-26, Center for Research in Economics and Statistics.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.

  62. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2017. "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls," Papers 1712.09089, arXiv.org, revised May 2021.

    Cited by:

    1. Masahiro Kato & Akari Ohda & Masaaki Imaizumi, 2023. "Asymptotically Unbiased Synthetic Control Methods by Distribution Matching," Papers 2307.11127, arXiv.org, revised May 2024.
    2. Eli Ben-Michael & Avi Feller & Jesse Rothstein, 2019. "Synthetic Controls with Staggered Adoption," Papers 1912.03290, arXiv.org, revised Jan 2021.
    3. Nuno Garoupa & Rok Spruk, 2024. "Populist Constitutional Backsliding and Judicial Independence: Evidence from Turkiye," Papers 2410.02439, arXiv.org.
    4. Billy Ferguson & Brad Ross, 2020. "Assessing the Sensitivity of Synthetic Control Treatment Effect Estimates to Misspecification Error," Papers 2012.15367, arXiv.org, revised Feb 2021.
    5. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    6. Ignacio Martinez & Jaume Vives-i-Bastida, 2022. "Bayesian and Frequentist Inference for Synthetic Controls," Papers 2206.01779, arXiv.org, revised Jul 2024.
    7. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2019. "Synthetic Difference In Differences," NBER Working Papers 25532, National Bureau of Economic Research, Inc.
    8. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    9. Ferman, Bruno, 2017. "Matching Estimators with Few Treated and Many Control Observations," MPRA Paper 78940, University Library of Munich, Germany.
    10. Joseph Fry, 2023. "A Method of Moments Approach to Asymptotically Unbiased Synthetic Controls," Papers 2312.01209, arXiv.org, revised Mar 2024.
    11. Alberto Abadie & Jinglong Zhao, 2021. "Synthetic Controls for Experimental Design," Papers 2108.02196, arXiv.org, revised Sep 2024.
    12. Lionel Fontagn'e & Francesca Micocci & Armando Rungi, 2024. "The heterogeneous impact of the EU-Canada agreement with causal machine learning," Papers 2407.07652, arXiv.org, revised Jul 2024.
    13. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2022. "Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data," Papers 2207.14481, arXiv.org, revised Oct 2022.
    14. Douglas Kiarelly Godoy de Araujo, 2024. "Synthetic controls with machine learning: application on the effect of labour deregulation on worker productivity in Brazil," BIS Working Papers 1181, Bank for International Settlements.
    15. Eli Ben-Michael & Avi Feller & Jesse Rothstein, 2018. "The Augmented Synthetic Control Method," Papers 1811.04170, arXiv.org, revised Jul 2020.
    16. Lucke, Bernd, 2022. "Growth Effects of European Monetary Union: A Synthetic Control Approach," MPRA Paper 120662, University Library of Munich, Germany, revised 27 Mar 2024.
    17. Bruno Ferman, 2021. "On the Properties of the Synthetic Control Estimator with Many Periods and Many Controls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1764-1772, October.
    18. Robert Messerle & Jonas Schreyögg, 2024. "Country-level effects of diagnosis-related groups: evidence from Germany’s comprehensive reform of hospital payments," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 25(6), pages 1013-1030, August.
    19. Michael Funke & Kadri Männasoo & Helery Tasane, 2023. "Regional Economic Impacts of the Øresund Cross-Border Fixed Link: Cui Bono?," CESifo Working Paper Series 10557, CESifo.
    20. Achille Nazaret & Claudia Shi & David M. Blei, 2023. "On the Misspecification of Linear Assumptions in Synthetic Control," Papers 2302.12777, arXiv.org.
    21. Carlos J. Charotti & Nuno Palma & João Pereira dos Santos, 2022. "American Treasure and the Decline of Spain," Economics Discussion Paper Series 2201, Economics, The University of Manchester.
    22. Doerr, Luisa & Dorn, Florian & Gaebler, Stefanie & Potrafke, Niklas, 2020. "How new airport infrastructure promotes tourism: evidence from a synthetic control approach in German regions," Munich Reprints in Economics 84767, University of Munich, Department of Economics.
    23. Priscila Espinosa & Daniel Aparicio-Pérez & Emili Tortosa-Ausina, 2023. "On the Impact of Next Generation EU Funds: A Regional Synthetic Control Method Approach," Working Papers 2023/07, Economics Department, Universitat Jaume I, Castellón (Spain).
    24. Giovanni Mellace & Alessandra Pasquini, 2022. "Mediation Analysis Synthetic Control," Temi di discussione (Economic working papers) 1389, Bank of Italy, Economic Research and International Relations Area.
    25. Peter Backus & Thien Nguyen, 2021. "The Effect of the Sex Buyer Law on the Market for Sex, Sexual Health and Sexual Violence," Economics Discussion Paper Series 2106, Economics, The University of Manchester.
    26. Alberto Abadie & Jaume Vives-i-Bastida, 2022. "Synthetic Controls in Action," Papers 2203.06279, arXiv.org.
    27. Ruiz, Miguel Haro & Schult, Christoph & Wunder, Christoph, 2024. "The effects of the Iberian exception mechanism on wholesale electricity prices and consumer inflation: A synthetic-controls approach," IWH Discussion Papers 5/2024, Halle Institute for Economic Research (IWH).
    28. Stefano, Roberta di & Mellace, Giovanni, 2020. "The inclusive synthetic control method," Discussion Papers on Economics 14/2020, University of Southern Denmark, Department of Economics.
    29. Klößner, Stefan & Pfeifer, Gregor, 2015. "Synthesizing Cash for Clunkers: Stabilizing the Car Market, Hurting the Environment," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113207, Verein für Socialpolitik / German Economic Association.
    30. Jorge Eduardo Pérez, 2022. "Visualization, identification, and estimation in linear panel event-study design," Colombian Stata Users' Group Meetings 2022 05, Stata Users Group.
    31. Claudia Shi & Dhanya Sridhar & Vishal Misra & David M. Blei, 2021. "On the Assumptions of Synthetic Control Methods," Papers 2112.05671, arXiv.org, revised Dec 2021.
    32. Demirci, Murat, 2023. "Youth responses to political populism: Education abroad as a step toward emigration," Journal of Comparative Economics, Elsevier, vol. 51(2), pages 653-673.
    33. Li, Xingyu & Shen, Yan & Zhou, Qiankun, 2024. "Confidence intervals of treatment effects in panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 240(1).
    34. Lea Bottmer & Guido Imbens & Jann Spiess & Merrill Warnick, 2021. "A Design-Based Perspective on Synthetic Control Methods," Papers 2101.09398, arXiv.org, revised Jul 2023.
    35. Bruno Ferman & Cristine Pinto, 2021. "Synthetic controls with imperfect pretreatment fit," Quantitative Economics, Econometric Society, vol. 12(4), pages 1197-1221, November.
    36. Shosei Sakaguchi & Hayato Tagawa, 2024. "Identification and Inference for Synthetic Control Methods with Spillover Effects: Estimating the Economic Cost of the Sudan Split," Papers 2408.00291, arXiv.org, revised Oct 2024.
    37. Yan Zhang & Zudi Lu, 2024. "A Time Series Synthetic Control Causal Evaluation of the UK’s Mini-Budget Policy on Stock Market," Mathematics, MDPI, vol. 12(20), pages 1-25, October.
    38. Jiafeng Chen, 2023. "Synthetic Control as Online Linear Regression," Econometrica, Econometric Society, vol. 91(2), pages 465-491, March.
    39. Barber, Andrew & West, Jeremy, 2022. "Conditional cash lotteries increase COVID-19 vaccination rates," Journal of Health Economics, Elsevier, vol. 81(C).
    40. Jonathan Roth & Pedro H. C. Sant'Anna & Alyssa Bilinski & John Poe, 2022. "What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature," Papers 2201.01194, arXiv.org, revised Jan 2023.
    41. Jianfei Cao & Connor Dowd, 2019. "Estimation and Inference for Synthetic Control Methods with Spillover Effects," Papers 1902.07343, arXiv.org, revised Nov 2019.
    42. Jiafeng Chen, 2022. "Synthetic Control As Online Linear Regression," Papers 2202.08426, arXiv.org, revised Nov 2022.
    43. Giovanni Peri & Derek Rury & Justin C. Wiltshire, 2024. "The Economic Impact of Migrants from Hurricane Maria," Journal of Human Resources, University of Wisconsin Press, vol. 59(6), pages 1795-1829.
    44. Erick Lahura & Rosario Sabrera, 2023. "The effect of infrastructure investment on tourism demand: a synthetic control approach for the case of Kuelap, Peru," Empirical Economics, Springer, vol. 65(1), pages 443-478, July.
    45. Luis A. F. Alvarez & Bruno Ferman, 2024. "On “Imputation of Counterfactual Outcomes when the Errors are Predictable'': Discussions on Misspecification and Suggestions of Sensitivity Analyses," Working Papers, Department of Economics 2024_16, University of São Paulo (FEA-USP).
    46. Kuosmanen, Timo & Zhou, Xun & Eskelinen, Juha & Malo, Pekka, 2021. "Design Flaw of the Synthetic Control Method," MPRA Paper 106328, University Library of Munich, Germany.
    47. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2018. "Exact and robust conformal inference methods for predictive machine learning with dependent data," CeMMAP working papers CWP16/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    48. Debdeep Chattopadhyay, 2023. "Did the Massachusetts Health Reform Program increase self-employment?," Empirical Economics, Springer, vol. 65(3), pages 1309-1344, September.
    49. Nicolaj S{o}ndergaard Muhlbach & Mikkel Slot Nielsen, 2019. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," Papers 1909.03968, arXiv.org, revised Feb 2021.
    50. Bruno Ferman, 2019. "Inference in Difference-in-Differences: How Much Should We Trust in Independent Clusters?," Papers 1909.01782, arXiv.org, revised Sep 2022.
    51. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    52. Viviano, Davide & Bradic, Jelena, 2023. "Synthetic Learner: Model-free inference on treatments over time," Journal of Econometrics, Elsevier, vol. 234(2), pages 691-713.
    53. Dmitry Arkhangelsky & David Hirshberg, 2023. "Large-Sample Properties of the Synthetic Control Method under Selection on Unobservables," Papers 2311.13575, arXiv.org, revised Dec 2023.
    54. Jason Poulos, 2019. "State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction," Papers 1903.08028, arXiv.org, revised Dec 2023.
    55. Matias D. Cattaneo & Yingjie Feng & Filippo Palomba & Rocio Titiunik, 2022. "scpi: Uncertainty Quantification for Synthetic Control Methods," Papers 2202.05984, arXiv.org, revised Oct 2022.
    56. Yinchu Zhu, 2019. "How well can we learn large factor models without assuming strong factors?," Papers 1910.10382, arXiv.org, revised Nov 2019.
    57. Silvia Goncalves & Serena Ng, 2024. "Imputation of Counterfactual Outcomes when the Errors are Predictable," Papers 2403.08130, arXiv.org, revised May 2024.
    58. Cong Wang, 2024. "Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component Analysis," Papers 2408.09271, arXiv.org, revised Sep 2024.
    59. Luis Alvarez & Bruno Ferman, 2023. "Extensions for Inference in Difference-in-Differences with Few Treated Clusters," Papers 2302.03131, arXiv.org.
    60. Isaiah Andrews & Drew Fudenberg & Lihua Lei & Annie Liang & Chaofeng Wu, 2022. "The Transfer Performance of Economic Models," Papers 2202.04796, arXiv.org, revised Jul 2024.
    61. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2019. "Inference on average treatment effects in aggregate panel data settings," CeMMAP working papers CWP32/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    62. Vivek F. Farias & Andrew A. Li & Tianyi Peng, 2021. "Learning Treatment Effects in Panels with General Intervention Patterns," Papers 2106.02780, arXiv.org, revised Mar 2023.
    63. Luis Costa & Vivek F. Farias & Patricio Foncea & Jingyuan (Donna) Gan & Ayush Garg & Ivo Rosa Montenegro & Kumarjit Pathak & Tianyi Peng & Dusan Popovic, 2023. "Generalized Synthetic Control for TestOps at ABI: Models, Algorithms, and Infrastructure," Interfaces, INFORMS, vol. 53(5), pages 336-349, September.
    64. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2023. "Same Root Different Leaves: Time Series and Cross‐Sectional Methods in Panel Data," Econometrica, Econometric Society, vol. 91(6), pages 2125-2154, November.
    65. Lucke, Bernd, 2022. "Growth Effects of European Monetary Union: A Synthetic Control Approach," MPRA Paper 115373, University Library of Munich, Germany.
    66. Zongwu Cai & Ying Fang & Ming Lin & Zixuan Wu, 2023. "A Quasi Synthetic Control Method for Nonlinear Models With High-Dimensional Covariates," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202305, University of Kansas, Department of Economics, revised Aug 2023.
    67. Cao, Jing & Ho, Mun S. & Ma, Rong & Teng, Fei, 2021. "When carbon emission trading meets a regulated industry: Evidence from the electricity sector of China," Journal of Public Economics, Elsevier, vol. 200(C).
    68. Guido W. Imbens & Davide Viviano, 2023. "Identification and Inference for Synthetic Controls with Confounding," Papers 2312.00955, arXiv.org.
    69. Justin Wiltshire, 2021. "allsynth: Synthetic control bias-corrections utilities for Stata," 2021 Stata Conference 15, Stata Users Group.
    70. Melissa Dell, 2024. "Deep Learning for Economists," Papers 2407.15339, arXiv.org, revised Nov 2024.
    71. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2018. "A $t$-test for synthetic controls," Papers 1812.10820, arXiv.org, revised Jan 2024.
    72. Rong J. B. Zhu, 2023. "Synthetic Regressing Control Method," Papers 2306.02584, arXiv.org, revised Oct 2023.
    73. Hideki Shimada & Kenji Asano & Yu Nagai & Akito Ozawa, 2022. "Assessing the Impact of Offshore Wind Power Deployment on Fishery: A Synthetic Control Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 83(3), pages 791-829, November.
    74. David M. Ritzwoller & Joseph P. Romano & Azeem M. Shaikh, 2024. "Randomization Inference: Theory and Applications," Papers 2406.09521, arXiv.org.
    75. Sandro Heiniger, 2024. "Data-driven model selection within the matrix completion method for causal panel data models," Papers 2402.01069, arXiv.org.

  63. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul, 2017. "Confidence bands for coefficients in high dimensional linear models with error-in-variables," CeMMAP working papers 22/17, Institute for Fiscal Studies.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Xiaobo Wang & Jiayu Huang & Guosheng Yin & Jian Huang & Yuanshan Wu, 2023. "Double bias correction for high-dimensional sparse additive hazards regression with covariate measurement errors," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(1), pages 115-141, January.
    3. Fan, Jinlin & Zhang, Yaowu & Zhu, Liping, 2022. "Independence tests in the presence of measurement errors: An invariance law," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    4. Li, Mengyan & Li, Runze & Ma, Yanyuan, 2021. "Inference in high dimensional linear measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    5. Galea, Manuel & de Castro, Mário, 2017. "Robust inference in a linear functional model with replications using the t distribution," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 134-145.
    6. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul & Mathieu Rosenbaum & Alexandre B. Tsybakov, 2017. "Pivotal Estimation Via Self-Normalization for High-Dimensional Linear Models with Errors in Variables," Working Papers 2017-26, Center for Research in Economics and Statistics.

  64. Victor Chernozhukov & Iv'an Fern'andez-Val & Whitney Newey, 2017. "Nonseparable Multinomial Choice Models in Cross-Section and Panel Data," Papers 1706.08418, arXiv.org, revised May 2018.

    Cited by:

    1. Cizek, Pavel & Sadikoglu, Serhan, 2022. "Nonseparable Panel Models with Index Structure and Correlated Random Effects," Discussion Paper 2022-009, Tilburg University, Center for Economic Research.
    2. Wang, Ao, 2023. "Sieve BLP: A semi-nonparametric model of demand for differentiated products," Journal of Econometrics, Elsevier, vol. 235(2), pages 325-351.
    3. Victor Chernozhukov & Jerry A. Hausman & Whitney K. Newey, 2019. "Demand Analysis with Many Prices," NBER Working Papers 26424, National Bureau of Economic Research, Inc.
    4. Wang, Ao, 2020. "Identifying the Distribution of Random Coefficients in BLP Demand Models Using One Single Variation in Product Characteristics," The Warwick Economics Research Paper Series (TWERPS) 1304, University of Warwick, Department of Economics.

  65. Victor Chernozhukov & Alfred Galichon & Marc Hallin & Marc Henry, 2017. "Monge-Kantorovich Depth, Quantiles, Ranks, and Signs," SciencePo Working papers Main hal-03391975, HAL.

    Cited by:

    1. Marc Hallin & Daniel Hlubinka & Šárka Hudecová, 2023. "Efficient Fully Distribution-Free Center-Outward Rank Tests for Multiple-Output Regression and MANOVA," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(543), pages 1923-1939, July.
    2. Marcel Klatt & Axel Munk & Yoav Zemel, 2022. "Limit laws for empirical optimal solutions in random linear programs," Annals of Operations Research, Springer, vol. 315(1), pages 251-278, August.
    3. Dmitry Arkhangelsky, 2019. "Dealing with a Technological Bias: The Difference-in-Difference Approach," Working Papers wp2019_1903, CEMFI.
    4. Gunsilius, Florian F., 2023. "A condition for the identification of multivariate models with binary instruments," Journal of Econometrics, Elsevier, vol. 235(1), pages 220-238.
    5. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," SciencePo Working papers Main hal-03936221, HAL.
    6. Hamel, Andreas H. & Kostner, Daniel, 2018. "Cone distribution functions and quantiles for multivariate random variables," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 97-113.
    7. Marc Hallin & Hongjian Shi & Mathias Drton & Fang Han, 2021. "Center-Outward Sign- and Rank-Based Quadrant, Spearman, and Kendall Tests for Multivariate Independence," Working Papers ECARES 2021-27, ULB -- Universite Libre de Bruxelles.
    8. Manuel Arellano & Stéphane Bonhomme, 2019. "Recovering Latent Variables by Matching," Working Papers wp2019_1914, CEMFI.
    9. María Edo & Walter Sosa Escudero & Marcela Svarc, 2021. "A multidimensional approach to measuring the middle class," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(1), pages 139-162, March.
    10. Lixiong Li & Marc Henry, 2022. "Finite Sample Inference in Incomplete Models," Papers 2204.00473, arXiv.org, revised Apr 2024.
    11. Marc Hallin, 2021. "Measure Transportation and Statistical Decision Theory," Working Papers ECARES 2021-04, ULB -- Universite Libre de Bruxelles.
    12. Hudecová, Šárka & Šiman, Miroslav, 2022. "Multivariate ranks based on randomized lift-interdirections," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    13. Yanqin Fan & Marc Henry, 2020. "Vector copulas," Papers 2009.06558, arXiv.org, revised Apr 2021.
    14. Marc Hallin & Davide La Vecchia & Hang Liu, 2020. "Rank-Based Testing for Semiparametric VAR Models: a measure transportation approach," Working Papers ECARES 2020-47, ULB -- Universite Libre de Bruxelles.
    15. Florian Gunsilius & Susanne Schennach, 2023. "Independent Nonlinear Component Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(542), pages 1305-1318, April.
    16. M. Hallin & D. La Vecchia & H. Liu, 2022. "Center-Outward R-Estimation for Semiparametric VARMA Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(538), pages 925-938, April.
    17. Kotík, Lukáš & Hlubinka, Daniel, 2017. "A weighted localization of halfspace depth and its properties," Journal of Multivariate Analysis, Elsevier, vol. 157(C), pages 53-69.
    18. Alberto González-Sanz & Marc Hallin & Bodhisattva Sen, 2023. "Monotone Measure-Preserving Maps in Hilbert Spaces: Existence, Uniqueness, and Stability," Working Papers ECARES 2023-10, ULB -- Universite Libre de Bruxelles.
    19. Olivier Paul Faugeras & Ludger Rüschendorf, 2021. "Functional, randomized and smoothed multivariate quantile regions," Post-Print hal-03352330, HAL.
    20. Marc Hallin, 2018. "From Mahalanobis to Bregman via Monge and Kantorovich towards a “General Generalised Distance”," Working Papers ECARES 2018-12, ULB -- Universite Libre de Bruxelles.
    21. Fan, Yanqin & Henry, Marc, 2023. "Vector copulas," Journal of Econometrics, Elsevier, vol. 234(1), pages 128-150.
    22. Marc Hallin & Daniel Hlubinka & Sarka Hudecova, 2020. "Fully Distribution-free Center-outward Rank Tests for Multiple-output Regression and Manova," Working Papers ECARES 2020-32, ULB -- Universite Libre de Bruxelles.
    23. Jan Bierlant & Sven Buitendag & Eustasio Del Barrio & Marc Hallin, 2019. "Center-Outward Quantiles And The Measurement Of Multivariate Risk," Working Papers ECARES 2019-30, ULB -- Universite Libre de Bruxelles.
    24. Petra Laketa & Stanislav Nagy, 2022. "Halfspace depth for general measures: the ray basis theorem and its consequences," Statistical Papers, Springer, vol. 63(3), pages 849-883, June.
    25. Davy Paindaveine & Germain Van Bever, 2017. "Halfspace Depths for Scatter, Concentration and Shape Matrices," Working Papers ECARES ECARES 2017-19, ULB -- Universite Libre de Bruxelles.
    26. Marc Hallin & Hang Liu, 2022. "Center-outward Rank- and Sign-based VARMA Portmanteau Tests," Working Papers ECARES 2022-27, ULB -- Universite Libre de Bruxelles.
    27. Florian Gunsilius, 2018. "Point-identification in multivariate nonseparable triangular models," Papers 1806.09680, arXiv.org.
    28. Marc Hallin, 2018. "From Mahalanobis to Bregman via Monge and Kantorovich," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 135-146, December.
    29. Yanqin Fan & Marc Henry & Brendan Pass & Jorge A. Rivero, 2022. "Lorenz map, inequality ordering and curves based on multidimensional rearrangements," Papers 2203.09000, arXiv.org, revised Apr 2024.
    30. Faugeras, Olivier P. & Rüschendorf, Ludger, 2021. "Functional, randomized and smoothed multivariate quantile regions," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    31. Hongjian Shi & Mathias Drton & Marc Hallin & Fang Han, 2023. "Semiparametrically Efficient Tests of Multivariate Independence Using Center-Outward Quadrant, Spearman, and Kendall Statistics," Working Papers ECARES 2023-03, ULB -- Universite Libre de Bruxelles.
    32. Marc Hallin & H Lui & Thomas Verdebout, 2022. "Nonparametric Measure-transportation-based Methods for Directional Data," Working Papers ECARES 2022-18, ULB -- Universite Libre de Bruxelles.
    33. Marc Hallin & Gilles Mordant, 2021. "On the Finite-Sample Performance of Measure Transportation-Based Multivariate Rank Tests," Working Papers ECARES 2021-24, ULB -- Universite Libre de Bruxelles.
    34. Hongjian Shi & Marc Hallin & Mathias Drton & Fang Han, 2020. "Rate-Optimality of Consistent Distribution-Free Tests of Independence Based on Center-Outward Ranks and Signs," Working Papers ECARES 2020-23, ULB -- Universite Libre de Bruxelles.
    35. Eustasio Del Barrio & Juan Cuesta Albertos & Marc Hallin & Carlos Matran, 2018. "Smooth Cyclically Monotone Interpolation and Empirical Center-Outward Distribution Functions," Working Papers ECARES 2018-15, ULB -- Universite Libre de Bruxelles.
    36. Eustasio del Barrio & Alberto González-Sanz & Marc Hallin, 2022. "Nonparametric Multiple-Output Center-Outward Quantile Regression," Working Papers ECARES 2022-10, ULB -- Universite Libre de Bruxelles.
    37. Alfred Galichon & Bernard Salani'e, 2021. "Cupid's Invisible Hand: Social Surplus and Identification in Matching Models," Papers 2106.02371, arXiv.org, revised Jan 2023.
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    552. Lucas Zhang, 2024. "Continuous difference-in-differences with double/debiased machine learning," Papers 2408.10509, arXiv.org.
    553. Michael P. Leung & Pantelis Loupos, 2022. "Graph Neural Networks for Causal Inference Under Network Confounding," Papers 2211.07823, arXiv.org, revised Mar 2024.
    554. Heejun Shin & Joseph Antonelli, 2023. "Improved inference for doubly robust estimators of heterogeneous treatment effects," Biometrics, The International Biometric Society, vol. 79(4), pages 3140-3152, December.
    555. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression: Long Term, Mediated, and Time Varying Treatment Effects," Papers 2112.14249, arXiv.org, revised Mar 2024.
    556. Alberto Caron & Gianluca Baio & Ioanna Manolopoulou, 2022. "Estimating individual treatment effects using non‐parametric regression models: A review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1115-1149, July.
    557. Kai Feng & Han Hong, 2024. "Statistical Inference of Optimal Allocations I: Regularities and their Implications," Papers 2403.18248, arXiv.org, revised Apr 2024.
    558. Christopher D. Walker, 2023. "Parametrization, Prior Independence, and the Semiparametric Bernstein-von Mises Theorem for the Partially Linear Model," Papers 2306.03816, arXiv.org, revised Feb 2024.
    559. Johannes Jakubik & Stefan Feuerriegel, 2022. "Data‐driven allocation of development aid toward sustainable development goals: Evidence from HIV/AIDS," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2739-2756, June.

  67. Victor Chernozhukov & Iv'an Fern'andez-Val & Tetsuya Kaji, 2016. "Extremal Quantile Regression: An Overview," Papers 1612.06850, arXiv.org, revised Feb 2017.

    Cited by:

    1. Chao, Shih-Kang & Härdle, Wolfgang Karl & Yuan, Ming, 2020. "Factorisable Multitask Quantile Regression," IRTG 1792 Discussion Papers 2020-004, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Matthew A Masten & Alexandre Poirier, 2023. "Choosing exogeneity assumptions in potential outcome models," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 327-349.
    3. D’Haultfœuille, Xavier & Maurel, Arnaud & Zhang, Yichong, 2018. "Extremal quantile regressions for selection models and the black–white wage gap," Journal of Econometrics, Elsevier, vol. 203(1), pages 129-142.
    4. Matthew A. Masten & Alexandre Poirier & Linqi Zhang, 2024. "Assessing Sensitivity to Unconfoundedness: Estimation and Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(1), pages 1-13, January.
    5. Yuya Sasaki & Yulong Wang, 2022. "Fixed-k Inference for Conditional Extremal Quantiles," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 829-837, April.
    6. Xavier D’Haultfoeuille & Arnaud Maurel & Xiaoyun Qiu & Yichong Zhang, 2020. "Estimating selection models without an instrument with Stata," Stata Journal, StataCorp LP, vol. 20(2), pages 297-308, June.
    7. Firpo, Sergio & Galvao, Antonio F. & Pinto, Cristine & Poirier, Alexandre & Sanroman, Graciela, 2022. "GMM quantile regression," Journal of Econometrics, Elsevier, vol. 230(2), pages 432-452.
    8. Marian Vavra, 2023. "Bias-Correction in Time Series Quantile Regression Models," Working and Discussion Papers WP 3/2023, Research Department, National Bank of Slovakia.
    9. Matthias Fischer & Daniel Kraus & Marius Pfeuffer & Claudia Czado, 2017. "Stress Testing German Industry Sectors: Results from a Vine Copula Based Quantile Regression," Risks, MDPI, vol. 5(3), pages 1-13, July.

  68. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Central limit theorems and bootstrap in high dimensions," CeMMAP working papers 39/16, Institute for Fiscal Studies.

    Cited by:

    1. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Testing many moment inequalities," CeMMAP working papers CWP42/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Fabian Dunker & Konstantin Eckle & Katharina Proksch & Johannes Schmidt-Hieber, 2017. "Tests for qualitative features in the random coefficients model," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 225, Courant Research Centre PEG.
    3. Ebert, Johannes & Spokoiny, Vladimir & Suvorikova, Alexandra, 2018. "Construction of Non-asymptotic Confidence Sets in 2 -Wasserstein Space," IRTG 1792 Discussion Papers 2018-025, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    4. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings," CeMMAP working papers CWP38/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers 47/16, Institute for Fiscal Studies.
    6. Ruben Dezeure & Peter Bühlmann & Cun-Hui Zhang, 2017. "High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 685-719, December.
    7. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul, 2017. "Confidence bands for coefficients in high dimensional linear models with error-in-variables," CeMMAP working papers 22/17, Institute for Fiscal Studies.
    8. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers CWP47/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Naumov, A. & Spokoiny, V. & Ulyanovk, V., 2018. "Bootstrap Confidence Sets for Spectral Projectors of Sample Covariance," IRTG 1792 Discussion Papers 2018-024, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    11. Zhilova, Mayya, 2015. "Simultaneous likelihood-based bootstrap confidence sets for a large number of models," SFB 649 Discussion Papers 2015-031, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

  69. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings," CeMMAP working papers 38/16, Institute for Fiscal Studies.

    Cited by:

    1. Babii, Andrii, 2020. "Honest Confidence Sets In Nonparametric Iv Regression And Other Ill-Posed Models," Econometric Theory, Cambridge University Press, vol. 36(4), pages 658-706, August.
    2. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    3. Kato, Kengo & Sasaki, Yuya, 2019. "Uniform confidence bands for nonparametric errors-in-variables regression," Journal of Econometrics, Elsevier, vol. 213(2), pages 516-555.
    4. Jun Ma & Vadim Marmer & Artyom Shneyerov & Pai Xu, 2019. "Monotonicity-Constrained Nonparametric Estimation and Inference for First-Price Auctions," Papers 1909.12974, arXiv.org.
    5. Steland, Ansgar, 2024. "Flexible nonlinear inference and change-point testing of high-dimensional spectral density matrices," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    6. Jun Ma & Vadim Marmer & Zhengfei Yu, 2021. "Inference on Individual Treatment Effects in Nonseparable Triangular Models," Papers 2107.05559, arXiv.org, revised Feb 2023.
    7. Victor Chernozhukov & Iv'an Fern'andez-Val & Martin Weidner, 2018. "Network and Panel Quantile Effects Via Distribution Regression," Papers 1803.08154, arXiv.org, revised Jun 2020.
    8. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Ivan Fernandez-Val, 2016. "Conditional quantile processes based on series or many regressors," CeMMAP working papers 46/16, Institute for Fiscal Studies.
    9. Kurisu, Daisuke & Otsu, Taisuke, 2022. "On linearization of nonparametric deconvolution estimators for repeated measurements model," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    10. Ma, Jun & Marmer, Vadim & Shneyerov, Artyom, 2019. "Inference for first-price auctions with Guerre, Perrigne, and Vuong’s estimator," Journal of Econometrics, Elsevier, vol. 211(2), pages 507-538.
    11. Shi, Chengchun & Luo, Shikai & Zhu, Hongtu & Song, Rui, 2021. "An online sequential test for qualitative treatment effects," LSE Research Online Documents on Economics 112521, London School of Economics and Political Science, LSE Library.
    12. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul, 2017. "Confidence bands for coefficients in high dimensional linear models with error-in-variables," CeMMAP working papers 22/17, Institute for Fiscal Studies.
    13. Matias Cattaneo & Richard K. Crump & Weining Wang, 2024. "Beta-sorted portfolios," CeMMAP working papers 20/24, Institute for Fiscal Studies.
    14. Yuta Koike, 2023. "High-Dimensional Central Limit Theorems for Homogeneous Sums," Journal of Theoretical Probability, Springer, vol. 36(1), pages 1-45, March.
    15. Dong, Hao & Taylor, Luke, 2022. "Nonparametric Significance Testing In Measurement Error Models," Econometric Theory, Cambridge University Press, vol. 38(3), pages 454-496, June.
    16. Peccati, Giovanni & Turchi, Nicola, 2023. "The discrepancy between min–max statistics of Gaussian and Gaussian-subordinated matrices," Stochastic Processes and their Applications, Elsevier, vol. 158(C), pages 315-341.
    17. Kato, Kengo & Kurisu, Daisuke, 2020. "Bootstrap confidence bands for spectral estimation of Lévy densities under high-frequency observations," Stochastic Processes and their Applications, Elsevier, vol. 130(3), pages 1159-1205.
    18. Kato, Kengo & Sasaki, Yuya, 2018. "Uniform confidence bands in deconvolution with unknown error distribution," Journal of Econometrics, Elsevier, vol. 207(1), pages 129-161.
    19. Daisuke Kurisu & Taisuke Otsu, 2021. "On linearization of nonparametric deconvolution estimators for repeated measurements model," STICERD - Econometrics Paper Series 615, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    20. Byunghoon Kang, 2019. "Inference in Nonparametric Series Estimation with Specification Searches for the Number of Series Terms," Papers 1909.12162, arXiv.org, revised Feb 2020.
    21. Zincenko, Federico, 2024. "Estimation and inference of seller’s expected revenue in first-price auctions," Journal of Econometrics, Elsevier, vol. 241(1).
    22. Belloni, Alexandre & Hansen, Christian & Newey, Whitney, 2022. "High-dimensional linear models with many endogenous variables," Journal of Econometrics, Elsevier, vol. 228(1), pages 4-26.
    23. Kurisu, Daisuke & Otsu, Taisuke, 2022. "On linearization of nonparametric deconvolution estimators for repeated measurements model," LSE Research Online Documents on Economics 112676, London School of Economics and Political Science, LSE Library.

  70. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.

    Cited by:

    1. Matthew A. Masten & Alexandre Poirier, 2018. "Interpreting Quantile Independence," Papers 1804.10957, arXiv.org.
    2. Matthew A Masten & Alexandre Poirier, 2023. "Choosing exogeneity assumptions in potential outcome models," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 327-349.
    3. Hossein Alidaee & Eric Auerbach & Michael P. Leung, 2020. "Recovering Network Structure from Aggregated Relational Data using Penalized Regression," Papers 2001.06052, arXiv.org.

  71. Victor Chernozhukov & Chris Hansen & Martin Spindler, 2016. "hdm: High-Dimensional Metrics," Papers 1608.00354, arXiv.org.

    Cited by:

    1. Edward I. Altman & Marco Balzano & Alessandro Giannozzi & Stjepan Srhoj, 2023. "Revisiting SME default predictors: The Omega Score," Journal of Small Business Management, Taylor & Francis Journals, vol. 61(6), pages 2383-2417, November.
    2. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers 13/16, Institute for Fiscal Studies.
    3. Daniels, David P. & Zlatev, Julian J., 2019. "Choice architects reveal a bias toward positivity and certainty," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 132-149.
    4. Godzinski, Alexandre & Suarez Castillo, Milena, 2021. "Disentangling the effects of air pollutants with many instruments," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).
    5. Hannes Wallimann & David Imhof & Martin Huber, 2020. "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," Papers 2004.05629, arXiv.org.
    6. Ismael Mourifié, 2019. "A marriage matching function with flexible spillover and substitution patterns," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 67(2), pages 421-461, March.
    7. Stefan Seifert & Marica Valente, 2018. "An Offer that you Can't Refuse? Agrimafias and Migrant Labor on Vineyards in Southern Italy," Discussion Papers of DIW Berlin 1735, DIW Berlin, German Institute for Economic Research.
    8. Selina Gangl & Martin Huber, 2021. "From homemakers to breadwinners? How mandatory kindergarten affects maternal labour market outcomes," Papers 2111.14524, arXiv.org, revised Mar 2022.
    9. Harold D. Chiang, 2019. "Many Average Partial Effects: with an Application to Text Regression," 2019 Papers pch1836, Job Market Papers.
    10. Gangl, Selina & Huber, Martin, 2021. "From homemakers to breadwinners? How mandatory kindergarten affects maternal labour market attachment," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203636, Verein für Socialpolitik / German Economic Association, revised 2021.
    11. Elena Denisova-Schmidt & Martin Huber & Elvira Leontyeva & Anna Solovyeva, 2021. "Combining experimental evidence with machine learning to assess anti-corruption educational campaigns among Russian university students," Empirical Economics, Springer, vol. 60(4), pages 1661-1684, April.
    12. Ruben Dezeure & Peter Bühlmann & Cun-Hui Zhang, 2017. "High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 685-719, December.
    13. Pawel Dlotko & Simon Rudkin & Wanling Qiu, 2019. "Topologically Mapping the Macroeconomy," Papers 1911.10476, arXiv.org.
    14. Imhof, David & Wallimann, Hannes, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," International Review of Law and Economics, Elsevier, vol. 68(C).
    15. Marica Valente & Timm Gries & Lorenzo Trapani, 2023. "Informal employment from migration shocks," Working Papers 2023-09, Faculty of Economics and Statistics, Universität Innsbruck.
    16. Huber, Martin & Imhof, David, 2019. "Machine learning with screens for detecting bid-rigging cartels," International Journal of Industrial Organization, Elsevier, vol. 65(C), pages 277-301.
    17. Philipp Bach & Victor Chernozhukov & Martin Spindler, 2019. "Valid simultaneous inference in high-dimensional settings (with the HDM package for R)," CeMMAP working papers CWP30/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    19. Simone Maxand & Hend Sallam, 2024. "Local Fiscal Effects of Immigration in Germany," CESifo Working Paper Series 11162, CESifo.
    20. Abadie, Alberto & Gu, Jiaying & Shen, Shu, 2024. "Instrumental variable estimation with first-stage heterogeneity," Journal of Econometrics, Elsevier, vol. 240(2).

  72. Guillaume Carlier & Victor Chernozhukov & Alfred Galichon, 2016. "Vector Quantile Regression: An Optimal Transport Approach," SciencePo Working papers Main hal-03567920, HAL.

    Cited by:

    1. Florian Gunsilius & Susanne M. Schennach, 2017. "A nonlinear principal component decomposition," CeMMAP working papers CWP16/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Dmitry Arkhangelsky, 2019. "Dealing with a Technological Bias: The Difference-in-Difference Approach," Working Papers wp2019_1903, CEMFI.
    3. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," SciencePo Working papers Main hal-03936221, HAL.
    4. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    5. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Ozdemir, Huseyin & Wohar, Mark E., 2020. "Transmission of US and EU Economic Policy Uncertainty Shock to Asian Economies in Bad and Good Times," IZA Discussion Papers 13274, Institute of Labor Economics (IZA).
    6. Victor Chernozhukov & Alfred Galichon & Marc Hallin & Marc Henry, 2015. "Monge-Kantorovich Depth, Quantiles, Ranks, and Signs," Working Papers hal-03460056, HAL.
    7. Eduardo Rubén Scarano, "undated". "La consolidación de la economía como disciplina científica en el país. El registro estadístico permanente," Documentos de trabajo del Instituto Interdisciplinario de Economía Política IIEP (UBA-CONICET) 202271, Universidad de Buenos Aires, Facultad de Ciencias Económicas, Instituto Interdisciplinario de Economía Política IIEP (UBA-CONICET).
    8. Keisuke Hirano & Jack R. Porter, 2023. "Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits," Papers 2302.03117, arXiv.org.
    9. Richard Blundell & Dennis Kristensen & Rosa Matzkin, 2017. "Individual counterfactuals with multidimensional unobserved heterogeneity," CeMMAP working papers 60/17, Institute for Fiscal Studies.
    10. Nadja Klein & Thomas Kneib, 2020. "Directional bivariate quantiles: a robust approach based on the cumulative distribution function," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 225-260, June.
    11. Marc Hallin, 2021. "Measure Transportation and Statistical Decision Theory," Working Papers ECARES 2021-04, ULB -- Universite Libre de Bruxelles.
    12. Florian Gunsilius & Susanne Schennach, 2023. "Independent Nonlinear Component Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(542), pages 1305-1318, April.
    13. Tongseok Lim, 2023. "Replication of financial derivatives under extreme market models given marginals," Papers 2307.00807, arXiv.org.
    14. Richard Spady & Sami Stouli, 2012. "Dual Regression," Papers 1210.6958, arXiv.org, revised Sep 2018.
    15. Roger Koenker, 2017. "Quantile regression 40 years on," CeMMAP working papers CWP36/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Nadja Klein & Torsten Hothorn & Luisa Barbanti & Thomas Kneib, 2022. "Multivariate conditional transformation models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 116-142, March.
    17. Walter W. Zhang & Sanjog Misra, 2022. "Coarse Personalization," Papers 2204.05793, arXiv.org, revised Aug 2024.
    18. Daouia, Abdelaati & Paindaveine, Davy, 2019. "Multivariate Expectiles, Expectile Depth and Multiple-Output Expectile Regression," TSE Working Papers 19-1022, Toulouse School of Economics (TSE), revised Feb 2023.
    19. Hongjian Shi & Mathias Drton & Marc Hallin & Fang Han, 2023. "Semiparametrically Efficient Tests of Multivariate Independence Using Center-Outward Quadrant, Spearman, and Kendall Statistics," Working Papers ECARES 2023-03, ULB -- Universite Libre de Bruxelles.
    20. Agarwal, Gaurav & Tu, Wei & Sun, Ying & Kong, Linglong, 2022. "Flexible quantile contour estimation for multivariate functional data: Beyond convexity," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    21. Marc Hallin & Gilles Mordant, 2021. "On the Finite-Sample Performance of Measure Transportation-Based Multivariate Rank Tests," Working Papers ECARES 2021-24, ULB -- Universite Libre de Bruxelles.
    22. Timo Dimitriadis & Yannick Hoga, 2022. "Dynamic CoVaR Modeling," Papers 2206.14275, arXiv.org, revised Feb 2024.
    23. Ruodu Wang & Zhenyuan Zhang, 2022. "Simultaneous Optimal Transport," Papers 2201.03483, arXiv.org, revised Dec 2024.
    24. Daniel Hlubinka & Lukáš Kotík & Miroslav Šiman, 2022. "Multivariate quantiles with both overall and directional probability interpretation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1586-1604, December.
    25. Eustasio del Barrio & Alberto González-Sanz & Marc Hallin, 2022. "Nonparametric Multiple-Output Center-Outward Quantile Regression," Working Papers ECARES 2022-10, ULB -- Universite Libre de Bruxelles.
    26. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Working Papers hal-03936221, HAL.
    27. Carlier, Guillaume & Chernozhukov, Victor & Galichon, Alfred, 2017. "Vector quantile regression beyond the specified case," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 96-102.
    28. Hubner, Stefan, 2023. "Identification of unobserved distribution factors and preferences in the collective household model," Journal of Econometrics, Elsevier, vol. 234(1), pages 301-326.

  73. Victor Chernozhukov & Chris Hansen & Martin Spindler, 2016. "High-Dimensional Metrics in R," Papers 1603.01700, arXiv.org, revised Aug 2016.

    Cited by:

    1. Edward I. Altman & Marco Balzano & Alessandro Giannozzi & Stjepan Srhoj, 2023. "Revisiting SME default predictors: The Omega Score," Journal of Small Business Management, Taylor & Francis Journals, vol. 61(6), pages 2383-2417, November.
    2. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers 13/16, Institute for Fiscal Studies.
    3. Daniels, David P. & Zlatev, Julian J., 2019. "Choice architects reveal a bias toward positivity and certainty," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 132-149.
    4. Godzinski, Alexandre & Suarez Castillo, Milena, 2021. "Disentangling the effects of air pollutants with many instruments," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).
    5. Hannes Wallimann & David Imhof & Martin Huber, 2020. "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," Papers 2004.05629, arXiv.org.
    6. Ismael Mourifié, 2019. "A marriage matching function with flexible spillover and substitution patterns," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 67(2), pages 421-461, March.
    7. Michael C. Knaus, 2018. "A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills," Papers 1805.10300, arXiv.org, revised Jan 2019.
    8. Stefan Seifert & Marica Valente, 2018. "An Offer that you Can't Refuse? Agrimafias and Migrant Labor on Vineyards in Southern Italy," Discussion Papers of DIW Berlin 1735, DIW Berlin, German Institute for Economic Research.
    9. Höschle, Lisa & Trestini, Samuele & Giampietri, Elisa, 2022. "Participation in a mutual fund covering losses due to pest infestation: analyzing key predictors of farmers’ interest through machine learning," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 26(3), December.
    10. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
    11. Selina Gangl & Martin Huber, 2021. "From homemakers to breadwinners? How mandatory kindergarten affects maternal labour market outcomes," Papers 2111.14524, arXiv.org, revised Mar 2022.
    12. Harold D. Chiang, 2019. "Many Average Partial Effects: with an Application to Text Regression," 2019 Papers pch1836, Job Market Papers.
    13. Gangl, Selina & Huber, Martin, 2021. "From homemakers to breadwinners? How mandatory kindergarten affects maternal labour market attachment," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203636, Verein für Socialpolitik / German Economic Association, revised 2021.
    14. Elena Denisova-Schmidt & Martin Huber & Elvira Leontyeva & Anna Solovyeva, 2021. "Combining experimental evidence with machine learning to assess anti-corruption educational campaigns among Russian university students," Empirical Economics, Springer, vol. 60(4), pages 1661-1684, April.
    15. Ruben Dezeure & Peter Bühlmann & Cun-Hui Zhang, 2017. "High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 685-719, December.
    16. Pawel Dlotko & Simon Rudkin & Wanling Qiu, 2019. "Topologically Mapping the Macroeconomy," Papers 1911.10476, arXiv.org.
    17. Imhof, David & Wallimann, Hannes, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," International Review of Law and Economics, Elsevier, vol. 68(C).
    18. Huber, Martin & Imhof, David, 2019. "Machine learning with screens for detecting bid-rigging cartels," International Journal of Industrial Organization, Elsevier, vol. 65(C), pages 277-301.
    19. Philipp Bach & Victor Chernozhukov & Martin Spindler, 2019. "Valid simultaneous inference in high-dimensional settings (with the HDM package for R)," CeMMAP working papers CWP30/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.

  74. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Comparison and anti-concentration bounds for maxima of Gaussian random vectors," CeMMAP working papers 40/16, Institute for Fiscal Studies.

    Cited by:

    1. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Testing many moment inequalities," CeMMAP working papers CWP42/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Liang, Chong & Schienle, Melanie, 2019. "Determination of vector error correction models in high dimensions," Working Paper Series in Economics 124, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    3. Donald W.K. Andrews & Xiaoxia Shi, 2015. "Inference Based on Many Conditional Moment Inequalities," Cowles Foundation Discussion Papers 2010, Cowles Foundation for Research in Economics, Yale University.
    4. Demian Pouzo, 2014. "Bootstrap Consistency for Quadratic Forms of Sample Averages with Increasing Dimension," Papers 1411.2701, arXiv.org, revised Aug 2015.

  75. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2016. "Locally Robust Semiparametric Estimation," Papers 1608.00033, arXiv.org, revised Aug 2020.

    Cited by:

    1. Sokbae Lee & Ryo Okui & Yoon†Jae Whang, 2017. "Doubly robust uniform confidence band for the conditional average treatment effect function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1207-1225, November.
    2. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    3. Jiafeng Chen & David M. Ritzwoller, 2021. "Semiparametric Estimation of Long-Term Treatment Effects," Papers 2107.14405, arXiv.org, revised Aug 2023.
    4. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Neng-Chieh Chang, 2020. "The Mode Treatment Effect," Papers 2007.11606, arXiv.org.
    6. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    7. Cordier, J.; & Salvi, I.; & Steinbeck, V.; & Geissler, A.; & Vogel, J.;, 2023. "Is rapid recovery always the best recovery? - Developing a machine learning approach for optimal assignment rules under capacity constraints for knee replacement patients," Health, Econometrics and Data Group (HEDG) Working Papers 23/08, HEDG, c/o Department of Economics, University of York.
    8. Yi Zhang & Eli Ben-Michael & Kosuke Imai, 2022. "Safe Policy Learning under Regression Discontinuity Designs with Multiple Cutoffs," Papers 2208.13323, arXiv.org, revised Sep 2024.
    9. Du, Zaichao & Escanciano, Juan Carlos & Zhu, Guangwei, 2023. "The case for CASE: Estimating heterogeneous systemic effects," Journal of Banking & Finance, Elsevier, vol. 157(C).
    10. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org, revised Nov 2024.
    11. Lewbel, Arthur & Choi, Jin Young & Zhou, Zhuzhu, 2023. "Over-identified Doubly Robust identification and estimation," Journal of Econometrics, Elsevier, vol. 235(1), pages 25-42.
    12. Abhinandan Dalal & Patrick Blobaum & Shiva Kasiviswanathan & Aaditya Ramdas, 2024. "Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters," Papers 2408.09598, arXiv.org, revised Sep 2024.
    13. Santiago Pereda Fernández, 2019. "Identification and estimation of triangular models with a binary treatment," Temi di discussione (Economic working papers) 1210, Bank of Italy, Economic Research and International Relations Area.
    14. Daniel Jacob, 2021. "CATE meets ML," Digital Finance, Springer, vol. 3(2), pages 99-148, June.
    15. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    16. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    17. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
    18. Susan Athey & Stefan Wager, 2017. "Policy Learning with Observational Data," Papers 1702.02896, arXiv.org, revised Sep 2020.
    19. Mert Demirer & Vasilis Syrgkanis & Greg Lewis & Victor Chernozhukov, 2019. "Semi-Parametric Efficient Policy Learning with Continuous Actions," Papers 1905.10116, arXiv.org, revised Jul 2019.
    20. Cattaneo, Matias D & Jansson, Michael & Ma, Xinwei, 2021. "Local regression distribution estimators," Department of Economics, Working Paper Series qt7416d3x8, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    21. Jacob, Daniel, 2020. "Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects," IRTG 1792 Discussion Papers 2020-014, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    22. Hidehiko Ichimura & Whitney K. Newey, 2017. "The influence function of semiparametric estimators," CeMMAP working papers 06/17, Institute for Fiscal Studies.
    23. Chaudhuri, Saraswata & Renault, Eric, 2023. "Efficient estimation of regression models with user-specified parametric model for heteroskedasticty," The Warwick Economics Research Paper Series (TWERPS) 1473, University of Warwick, Department of Economics.
    24. Gyungbae Park, 2024. "Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions," Papers 2403.15934, arXiv.org.
    25. Daniel Jacob, 2021. "CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning," Papers 2104.09935, arXiv.org, revised Apr 2021.
    26. Sant’Anna, Pedro H.C. & Song, Xiaojun, 2019. "Specification tests for the propensity score," Journal of Econometrics, Elsevier, vol. 210(2), pages 379-404.
    27. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
    28. Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
    29. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    30. Jikai Jin & Vasilis Syrgkanis, 2024. "Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation," Papers 2402.14264, arXiv.org, revised Mar 2024.
    31. Undral Byambadalai & Tatsushi Oka & Shota Yasui, 2024. "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction," Papers 2407.16037, arXiv.org.
    32. Greg Lewis & Vasilis Syrgkanis, 2020. "Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation," Papers 2002.07285, arXiv.org, revised Jun 2021.
    33. Jelena Bradic & Victor Chernozhukov & Whitney K. Newey & Yinchu Zhu, 2019. "Minimax Semiparametric Learning With Approximate Sparsity," Papers 1912.12213, arXiv.org, revised Aug 2022.
    34. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.
    35. Zhengyuan Zhou & Susan Athey & Stefan Wager, 2018. "Offline Multi-Action Policy Learning: Generalization and Optimization," Papers 1810.04778, arXiv.org, revised Nov 2018.
    36. Thomas H. J{o}rgensen, 2020. "Sensitivity to Calibrated Parameters," Papers 2004.12100, arXiv.org, revised Mar 2021.
    37. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2021. "Linear IV regression estimators for structural dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 222(1), pages 778-804.
    38. Maximilian Blesch & Philipp Eisenhauer, 2021. "Robust decision-making under risk and ambiguity," Papers 2104.12573, arXiv.org, revised Oct 2021.
    39. Stéphane Bonhomme & Martin Weidner, 2020. "Minimizing Sensitivity to Model Misspecification," CeMMAP working papers CWP37/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    40. Xingyu Chen & Lin Liu & Rajarshi Mukherjee, 2024. "Method-of-Moments Inference for GLMs and Doubly Robust Functionals under Proportional Asymptotics," Papers 2408.06103, arXiv.org.
    41. Cattaneo, Matias D. & Jansson, Michael, 2022. "Average Density Estimators: Efficiency And Bootstrap Consistency," Econometric Theory, Cambridge University Press, vol. 38(6), pages 1140-1174, December.
    42. Rahul Singh, 2020. "Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments," Papers 2012.10315, arXiv.org, revised Mar 2023.
    43. Dylan J. Foster & Vasilis Syrgkanis, 2019. "Orthogonal Statistical Learning," Papers 1901.09036, arXiv.org, revised Jun 2023.
    44. Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022. "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers 2211.07903, arXiv.org.
    45. Matias D. Cattaneo & Michael Jansson, 2018. "Kernel†Based Semiparametric Estimators: Small Bandwidth Asymptotics and Bootstrap Consistency," Econometrica, Econometric Society, vol. 86(3), pages 955-995, May.
    46. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," CESifo Working Paper Series 9037, CESifo.
    47. Manu Navjeevan & Rodrigo Pinto & Andres Santos, 2023. "Identification and Estimation in a Class of Potential Outcomes Models," Papers 2310.05311, arXiv.org.
    48. Vira Semenova, 2020. "Generalized Lee Bounds," Papers 2008.12720, arXiv.org, revised Feb 2023.
    49. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    50. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    51. Stéphane Bonhomme & Martin Weidner, 2022. "Minimizing sensitivity to model misspecification," Quantitative Economics, Econometric Society, vol. 13(3), pages 907-954, July.
    52. Whitney K. Newey & James M. Robins, 2017. "Cross-fitting and fast remainder rates for semiparametric estimation," CeMMAP working papers CWP41/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    53. Minkyung Kim & K. Sudhir & Kosuke Uetake, 2022. "A Structural Model of a Multitasking Salesforce: Incentives, Private Information, and Job Design," Management Science, INFORMS, vol. 68(6), pages 4602-4630, June.
    54. Mengshan Xu & Taisuke Otsu, 2022. "Isotonic propensity score matching," Papers 2207.08868, arXiv.org, revised Aug 2024.
    55. Yang Ning & Sida Peng & Jing Tao, 2020. "Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data," Papers 2009.03151, arXiv.org.
    56. Bertille Antoine & Xiaolin Sun, 2020. "Partially Linear Models with Endogeneity: a conditional moment based approach," Discussion Papers dp20-06, Department of Economics, Simon Fraser University.
    57. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    58. Rahul Singh, 2021. "Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimension," Papers 2102.11076, arXiv.org, revised Jul 2024.
    59. Karun Adusumilli & Dita Eckardt, 2019. "Temporal-Difference estimation of dynamic discrete choice models," Papers 1912.09509, arXiv.org, revised Dec 2022.
    60. Sasaki, Yuya & Ura, Takuya, 2023. "Estimation and inference for policy relevant treatment effects," Journal of Econometrics, Elsevier, vol. 234(2), pages 394-450.
    61. Cattaneo, Matias D & Jansson, Michael & Ma, Xinwei, 2019. "Two-Step Estimation and Inference with Possibly Many Included Covariates," University of California at San Diego, Economics Working Paper Series qt86c7x315, Department of Economics, UC San Diego.
    62. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," THEMA Working Papers 2024-01, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    63. Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2021. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," Papers 2112.13398, arXiv.org, revised May 2024.
    64. Zhengyu Zhang & Zequn Jin & Lihua Lin, 2024. "Identification and inference of outcome conditioned partial effects of general interventions," Papers 2407.16950, arXiv.org.
    65. Doğan, Osman & Taşpınar, Süleyman & Bera, Anil K., 2021. "A Bayesian robust chi-squared test for testing simple hypotheses," Journal of Econometrics, Elsevier, vol. 222(2), pages 933-958.
    66. Chen, Jiafeng & Ritzwoller, David M., 2023. "Semiparametric estimation of long-term treatment effects," Journal of Econometrics, Elsevier, vol. 237(2).
    67. Taisuke Otsu & Mengshan Xu, 2022. "Isotonic propensity score matching," STICERD - Econometrics Paper Series 623, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    68. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
    69. Christopher D. Walker, 2024. "Semiparametric Bayesian Inference for a Conditional Moment Equality Model," Papers 2410.16017, arXiv.org.
    70. Riccardo D'Adamo, 2021. "Orthogonal Policy Learning Under Ambiguity," Papers 2111.10904, arXiv.org, revised Dec 2022.
    71. Anish Agarwal & Rahul Singh, 2021. "Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy," Papers 2107.02780, arXiv.org, revised Feb 2024.
    72. Nathan Kallus, 2023. "Treatment Effect Risk: Bounds and Inference," Management Science, INFORMS, vol. 69(8), pages 4579-4590, August.
    73. Rahul Singh & Liyang Sun, 2019. "Double Robustness for Complier Parameters and a Semiparametric Test for Complier Characteristics," Papers 1909.05244, arXiv.org, revised Dec 2022.
    74. Ben Deaner, 2021. "Many Proxy Controls," Papers 2110.03973, arXiv.org.
    75. Victor Chernozhukov & Whitney K Newey & Rahul Singh, 2018. "Automatic Debiased Machine Learning of Causal and Structural Effects," Papers 1809.05224, arXiv.org, revised Oct 2022.
    76. Jooyoung Cha & Harold D. Chiang & Yuya Sasaki, 2021. "Inference in high-dimensional regression models without the exact or $L^p$ sparsity," Papers 2108.09520, arXiv.org, revised Dec 2022.
    77. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2021. "A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees," Papers 2105.15197, arXiv.org, revised Oct 2022.
    78. Liu, Lin & Mukherjee, Rajarshi & Robins, James M., 2024. "Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators," Journal of Econometrics, Elsevier, vol. 240(2).
    79. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
    80. Neng-Chieh Chang, 2018. "Semiparametric Difference-in-Differences with Potentially Many Control Variables," Papers 1812.10846, arXiv.org, revised Jan 2019.
    81. Denis Chetverikov & Yukun Liu & Aleh Tsyvinski, 2022. "Weighted-average quantile regression," Papers 2203.03032, arXiv.org.
    82. Victor Chernozhukov & Whitney K. Newey & James Robins, 2018. "Double/de-biased machine learning using regularized Riesz representers," CeMMAP working papers CWP15/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    83. Keith Battocchi & Eleanor Dillon & Maggie Hei & Greg Lewis & Miruna Oprescu & Vasilis Syrgkanis, 2021. "Estimating the Long-Term Effects of Novel Treatments," Papers 2103.08390, arXiv.org, revised Feb 2022.
    84. Qizhao Chen & Vasilis Syrgkanis & Morgane Austern, 2022. "Debiased Machine Learning without Sample-Splitting for Stable Estimators," Papers 2206.01825, arXiv.org, revised Nov 2022.
    85. Victor Chernozhukov & Vira Semenova, 2018. "Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions," CeMMAP working papers CWP40/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    86. Liu, Yanghui & Li, Yehua & Carroll, Raymond J. & Wang, Naisyin, 2022. "Predictive functional linear models with diverging number of semiparametric single-index interactions," Journal of Econometrics, Elsevier, vol. 230(2), pages 221-239.
    87. Wei, Bo & Tan, Kean Ming & He, Xuming, 2024. "Estimation of complier expected shortfall treatment effects with a binary instrumental variable," Journal of Econometrics, Elsevier, vol. 238(2).
    88. Zhan Gao & Ji Hyung Lee & Ziwei Mei & Zhentao Shi, 2024. "Econometric Inference for High Dimensional Predictive Regressions," Papers 2409.10030, arXiv.org, revised Nov 2024.
    89. Krantz, Sebastian, 2024. "Mapping Africa's infrastructure potential with geospatial big data and causal ML," Kiel Working Papers 2276, Kiel Institute for the World Economy (IfW Kiel).
    90. Rahul Singh & Liyuan Xu & Arthur Gretton, 2021. "Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves," Papers 2111.03950, arXiv.org, revised Jul 2023.
    91. Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2018. "Deep Neural Networks for Estimation and Inference," Papers 1809.09953, arXiv.org, revised Sep 2019.
    92. Esfandiar Maasoumi & Jianqiu Wang & Zhuo Wang & Ke Wu, 2024. "Identifying factors via automatic debiased machine learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 438-461, April.
    93. Yusuke Narita & Shota Yasui & Kohei Yata, 2020. "Debiased Off-Policy Evaluation for Recommendation Systems," Papers 2002.08536, arXiv.org, revised Aug 2021.
    94. Pietro Emilio Spini, 2021. "Robustness, Heterogeneous Treatment Effects and Covariate Shifts," Papers 2112.09259, arXiv.org, revised Aug 2024.
    95. Kim, Bora & Lee, Myoung-jae, 2024. "Instrument-residual estimator for multi-valued instruments under full monotonicity," Statistics & Probability Letters, Elsevier, vol. 213(C).
    96. Xinwei Ma & Jingshen Wang, 2018. "Robust Inference Using Inverse Probability Weighting," Papers 1810.11397, arXiv.org, revised May 2019.
    97. Maximilian Blesch & Philipp Eisenhauer, 2021. "Robust Decision-Making Under Risk and Ambiguity," ECONtribute Discussion Papers Series 104, University of Bonn and University of Cologne, Germany.
    98. David M. Ritzwoller & Vasilis Syrgkanis, 2024. "Simultaneous Inference for Local Structural Parameters with Random Forests," Papers 2405.07860, arXiv.org, revised Sep 2024.
    99. Whitney K. Newey & James M. Robins, 2017. "Cross-fitting and fast remainder rates for semiparametric estimation," CeMMAP working papers 41/17, Institute for Fiscal Studies.
    100. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression: Long Term, Mediated, and Time Varying Treatment Effects," Papers 2112.14249, arXiv.org, revised Mar 2024.
    101. Kai Feng & Han Hong, 2024. "Statistical Inference of Optimal Allocations I: Regularities and their Implications," Papers 2403.18248, arXiv.org, revised Apr 2024.
    102. Vasilis Syrgkanis & Ruohan Zhan, 2023. "Post Reinforcement Learning Inference," Papers 2302.08854, arXiv.org, revised May 2024.

  76. Victor Chernozhukov & Iv'an Fern'andez-Val & Blaise Melly & Kaspar Wuthrich, 2016. "Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes," Papers 1608.05142, arXiv.org, revised Aug 2018.

    Cited by:

    1. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey & Sami Stouli & Francis Vella, 2017. "Semiparametric estimation of structural functions in nonseparable triangular models," CeMMAP working papers CWP48/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Kaspar Wuthrich & Ying Zhu, 2019. "Omitted variable bias of Lasso-based inference methods: A finite sample analysis," Papers 1903.08704, arXiv.org, revised Sep 2021.
    3. Victor Chernozhukov & Ivan Fernandez-Val & Siyi Luo, 2018. "Distribution regression with sample selection, with an application to wage decompositions in the UK," CeMMAP working papers CWP68/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    5. Valentina Corradi & Daniel Gutknecht, 2019. "Testing for Quantile Sample Selection," Papers 1907.07412, arXiv.org, revised Jan 2021.
    6. Lamarche, Carlos & Shi, Xuan & Young, Derek S., 2024. "Conditional Quantile Functions for Zero-Inflated Longitudinal Count Data," Econometrics and Statistics, Elsevier, vol. 31(C), pages 49-65.
    7. Tatsushi Oka & Shota Yasui & Yuta Hayakawa & Undral Byambadalai, 2024. "Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trials," Papers 2407.14074, arXiv.org.
    8. Victor Chernozhukov & Iv'an Fern'andez-Val & Martin Weidner, 2018. "Network and Panel Quantile Effects Via Distribution Regression," Papers 1803.08154, arXiv.org, revised Jun 2020.
    9. Ferdi Botha & John P. de New, 2020. "COVID-19 infections, labour market shocks, and subjective well-being," Melbourne Institute Working Paper Series wp2020n14, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    10. Chernozhukov, Victor & Wüthrich, Kaspar & Zhu, Yinchu, 2021. "Distributional conformal prediction," University of California at San Diego, Economics Working Paper Series qt2zs6m5p5, Department of Economics, UC San Diego.
    11. Ferdi Botha & John P. de New & Sonja C. de New & David C. Ribar & Nicolás Salamanca, 2020. "COVID-19 labour market shocks and their inequality implications for financial wellbeing," Melbourne Institute Working Paper Series wp2020n15, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    12. Victor Chernozhukov & Ivan Fernandez-Val & Siyi Luo, 2023. "Distribution regression with sample selection and UK wage decomposition," CeMMAP working papers 09/23, Institute for Fiscal Studies.

  77. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.

    Cited by:

    1. Xing, Lu & Han, DongHao & Hui, Xie, 2023. "The impact of carbon policy on corporate risk-taking with a double/debiased machine learning based difference-in-differences approach," Finance Research Letters, Elsevier, vol. 58(PC).
    2. Delprato, Marcos & Frola, Alessia & Antequera, Germán, 2022. "Indigenous and non-Indigenous proficiency gaps for out-of-school and in-school populations: A machine learning approach," International Journal of Educational Development, Elsevier, vol. 93(C).
    3. Pedro H. C. Sant'Anna & Jun B. Zhao, 2018. "Doubly Robust Difference-in-Differences Estimators," Papers 1812.01723, arXiv.org, revised May 2020.
    4. Xiong, Ruoxuan & Koenecke, Allison & Powell, Michael & Shen, Zhu & Vogelstein, Joshua T. & Athey, Susan, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Research Papers 3990, Stanford University, Graduate School of Business.
    5. Victor Chernozhukov & Mert Demirer & Esther Duflo & Ivan Fernandez-Val, 2017. "Generic machine learning inference on heterogenous treatment effects in randomized experiments," CeMMAP working papers CWP61/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Tinbergen Institute Discussion Papers 21-001/V, Tinbergen Institute.
    7. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org, revised Nov 2024.
    8. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).
    9. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2020. "Unconditional Quantile Regression with High Dimensional Data," Papers 2007.13659, arXiv.org, revised Feb 2022.
    10. Maximilian Maurice Gail & Phil-Adrian Klotz, 2021. "The Impact of the Agency Model on E-book Prices: Evidence from the UK," MAGKS Papers on Economics 202111, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    11. Oliver Hines & Stijn Vansteelandt & Karla Diaz-Ordaz, 2021. "Robust Inference for Mediated Effects in Partially Linear Models," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 595-618, June.
    12. Jong Hee Park & Byung Koo Kim, 2020. "Why your neighbor matters: Positions in preferential trade agreement networks and export growth in global value chains," Economics and Politics, Wiley Blackwell, vol. 32(3), pages 381-410, November.
    13. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
    14. Vira Semenova, 2017. "Debiased Machine Learning of Set-Identified Linear Models," Papers 1712.10024, arXiv.org, revised Dec 2022.
    15. Ziwei Cong & Jia Liu & Puneet Manchanda, 2021. "The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest," Papers 2107.01629, arXiv.org, revised Sep 2022.
    16. Amit Sharma & Emre Kiciman, 2020. "DoWhy: An End-to-End Library for Causal Inference," Papers 2011.04216, arXiv.org.
    17. Feng, Sanying & Kong, Kaidi & Kong, Yinfei & Li, Gaorong & Wang, Zhaoliang, 2022. "Statistical inference of heterogeneous treatment effect based on single-index model," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    18. Susan Athey & Guido Imbens & Jonas Metzger & Evan Munro, 2019. "Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations," Papers 1909.02210, arXiv.org, revised Jul 2020.
    19. Miquel Oliu-Barton & Bary S R Pradelski & Nicolas Woloszko & Lionel Guetta-Jeanrenaud & Philippe Aghion & Patrick Artus & Arnaud Fontanet & Philippe Martin & Guntram B Wolff, 2022. "The Effect of COVID Certificates on Vaccine Uptake, Health Outcomes, and the Economy," Post-Print hal-03813557, HAL.
    20. Victor Chernozhukov & Whitney Newey & Vira Semenova, 2019. "Welfare Analysis in Dynamic Models," Papers 1908.09173, arXiv.org, revised Nov 2024.
    21. Waverly Wei & Maya Petersen & Mark J van der Laan & Zeyu Zheng & Chong Wu & Jingshen Wang, 2023. "Efficient targeted learning of heterogeneous treatment effects for multiple subgroups," Biometrics, The International Biometric Society, vol. 79(3), pages 1934-1946, September.
    22. Jinyong Hahn & Jerry Hausman, 2021. "Problems with the Control Variable Approach in Achieving Unbiased Estimates in Nonlinear Models in the Presence of Many Instruments," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 39-58, December.
    23. Sven Klaassen & Jannis Kuck & Martin Spindler & Victor Chernozhukov, 2018. "Uniform Inference in High-Dimensional Gaussian Graphical Models," Papers 1808.10532, arXiv.org, revised Dec 2018.
    24. Wang, Hongning & Ma, Sanjun, 2022. "Preventing crimes against public health with artificial intelligence and machine learning capabilities," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    25. Yong Bian & Xiqian Wang & Qin Zhang, 2023. "How Does China's Household Portfolio Selection Vary with Financial Inclusion?," Papers 2311.01206, arXiv.org.
    26. Monica Andini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Viola Salvestrini, 2017. "Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy," Temi di discussione (Economic working papers) 1158, Bank of Italy, Economic Research and International Relations Area.
    27. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
    28. Jules Gazeaud & Nausheen Khan & Eric Mvukiyehe & Olivier Sterck, 2023. "With or without him? Experimental evidence on cash grants and gender-sensitive trainings in Tunisia," Post-Print hal-04364356, HAL.
    29. Sven Klaassen & Jannis Kueck & Martin Spindler, 2017. "Transformation Models in High-Dimensions," Papers 1712.07364, arXiv.org.
    30. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
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    32. Francesco Decarolis & Cristina Giorgiantonio, 2020. "Corruption red flags in public procurement: new evidence from Italian calls for tenders," Questioni di Economia e Finanza (Occasional Papers) 544, Bank of Italy, Economic Research and International Relations Area.
    33. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2023. "Source Condition Double Robust Inference on Functionals of Inverse Problems," Papers 2307.13793, arXiv.org.
    34. Vira Semenova, 2018. "Machine Learning for Dynamic Discrete Choice," Papers 1808.02569, arXiv.org, revised Nov 2018.
    35. Carl Bonander & Mikael Svensson, 2021. "Using causal forests to assess heterogeneity in cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 30(8), pages 1818-1832, August.
    36. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    37. Huber, Martin & Wüthrich, Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," University of California at San Diego, Economics Working Paper Series qt4j29d8sc, Department of Economics, UC San Diego.
    38. Valente, Marica, 2023. "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
    39. Maria Cuellar & Edward H. Kennedy, 2020. "A non‐parametric projection‐based estimator for the probability of causation, with application to water sanitation in Kenya," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1793-1818, October.
    40. Paolo Libenzio Brignoli & Alessandro Varacca & Cornelis Gardebroek & Paolo Sckokai, 2024. "Machine learning to predict grains futures prices," Agricultural Economics, International Association of Agricultural Economists, vol. 55(3), pages 479-497, May.
    41. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Papers 2101.00878, arXiv.org.
    42. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2017. "Non-separable Models with High-dimensional Data," Economics and Statistics Working Papers 15-2017, Singapore Management University, School of Economics.
    43. Andres Algaba & David Ardia & Keven Bluteau & Samuel Borms & Kris Boudt, 2020. "Econometrics Meets Sentiment: An Overview Of Methodology And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 34(3), pages 512-547, July.
    44. Andr'es Ram'irez-Hassan & Raquel Vargas-Correa & Gustavo Garc'ia & Daniel Londo~no, 2020. "Optimal selection of the number of control units in kNN algorithm to estimate average treatment effects," Papers 2008.06564, arXiv.org.
    45. Anna Baiardi & Andrea A Naghi, 2024. "The value added of machine learning to causal inference: evidence from revisited studies," The Econometrics Journal, Royal Economic Society, vol. 27(2), pages 213-234.
    46. Zhen Li & Jie Chen & Eric Laber & Fang Liu & Richard Baumgartner, 2023. "Optimal Treatment Regimes: A Review and Empirical Comparison," International Statistical Review, International Statistical Institute, vol. 91(3), pages 427-463, December.
    47. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    48. Whitney K. Newey & James M. Robins, 2017. "Cross-fitting and fast remainder rates for semiparametric estimation," CeMMAP working papers CWP41/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    49. Julia Hatamyar & Noemi Kreif, 2023. "Policy Learning with Rare Outcomes," Papers 2302.05260, arXiv.org, revised Oct 2023.
    50. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    51. Wang, Xiqian & Bian, Yong & Zhang, Qin, 2023. "The effect of cooking fuel choice on the elderly’s well-being: Evidence from two non-parametric methods," Energy Economics, Elsevier, vol. 125(C).
    52. Elliott Ash & Daniel L. Chen & Sergio Galletta, 2022. "Measuring Judicial Sentiment: Methods and Application to US Circuit Courts," Economica, London School of Economics and Political Science, vol. 89(354), pages 362-376, April.
    53. Heiler, Phillip & Kazak, Ekaterina, 2021. "Valid inference for treatment effect parameters under irregular identification and many extreme propensity scores," Journal of Econometrics, Elsevier, vol. 222(2), pages 1083-1108.
    54. Pradhi Aggarwal & Alec Brandon & Ariel Goldszmidt & Justin Holz & John List & Ian Muir & Gregory Sun & Thomas Yu, 2022. "High-frequency location data shows that race affects the likelihood of being stopped and fined for speeding," Natural Field Experiments 00764, The Field Experiments Website.
    55. Songul Cinaroglu, 2020. "Modelling unbalanced catastrophic health expenditure data by using machine‐learning methods," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(4), pages 168-181, October.
    56. Jiaming Mao & Zhesheng Zheng, 2020. "Structural Regularization," Papers 2004.12601, arXiv.org, revised Jun 2020.
    57. Matt Goldman & Brian Quistorff, 2018. "Pricing Engine: Estimating Causal Impacts in Real World Business Settings," Papers 1806.03285, arXiv.org, revised Jun 2018.
    58. Nikolaos Ignatiadis & Wolfgang Huber, 2021. "Covariate powered cross‐weighted multiple testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 720-751, September.
    59. Miruna Oprescu & Vasilis Syrgkanis & Zhiwei Steven Wu, 2018. "Orthogonal Random Forest for Causal Inference," Papers 1806.03467, arXiv.org, revised Sep 2019.
    60. Gareth Liu-Evans & Shalini Mitra, 2023. "Formal sector enforcement and welfare," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 30(3), pages 706-728, June.
    61. Jiaming Mao & Jingzhi Xu, 2020. "Ensemble Learning with Statistical and Structural Models," Papers 2006.05308, arXiv.org.
    62. Mochen Yang & Edward McFowland & Gordon Burtch & Gediminas Adomavicius, 2022. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 138-155, October.
    63. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    64. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," THEMA Working Papers 2024-01, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    65. Fernando Delbianco & Fernando Tohmé, 2023. "Individualized Conformal," Working Papers 247, Red Nacional de Investigadores en Economía (RedNIE).
    66. Sander Gerritsen & Mark Kattenberg & Sonny Kuijpers, 2019. "The impact of age at arrival on education and mental health," CPB Discussion Paper 389, CPB Netherlands Bureau for Economic Policy Analysis.
    67. Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2021. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," Papers 2112.13398, arXiv.org, revised May 2024.
    68. Fukuyama, Hirofumi & Tsionas, Mike & Tan, Yong, 2023. "Dynamic network data envelopment analysis with a sequential structure and behavioural-causal analysis: Application to the Chinese banking industry," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1360-1373.
    69. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.
    70. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    71. Jonas Metzger, 2022. "Adversarial Estimators," Papers 2204.10495, arXiv.org, revised Jun 2022.
    72. Dongcheng Zhang & Kunpeng Zhang, 2020. "Weighting-Based Treatment Effect Estimation via Distribution Learning," Papers 2012.13805, arXiv.org, revised May 2023.
    73. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
    74. Alexander Buchholz & Vito Bellini & Giuseppe Di Benedetto & Yannik Stein & Matteo Ruffini & Fabian Moerchen, 2022. "Fair Effect Attribution in Parallel Online Experiments," Papers 2210.08338, arXiv.org.
    75. Duncan Simester & Artem Timoshenko & Spyros I. Zoumpoulis, 2020. "Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges," Management Science, INFORMS, vol. 66(6), pages 2495-2522, June.
    76. Mei, Ziwei & Shi, Zhentao, 2024. "On LASSO for high dimensional predictive regression," Journal of Econometrics, Elsevier, vol. 242(2).
    77. Heigle, Julia & Pfeiffer, Friedhelm, 2019. "An analysis of selected labor market outcomes of college dropouts in Germany: A machine learning estimation approach. Research report," ZEW Expertises, ZEW - Leibniz Centre for European Economic Research, number 222378.
    78. Krantz, Sebastian, 2024. "Mapping Africa's infrastructure potential with geospatial big data and causal ML," Kiel Working Papers 2276, Kiel Institute for the World Economy (IfW Kiel).
    79. Belloni, Alexandre & Hansen, Christian & Newey, Whitney, 2022. "High-dimensional linear models with many endogenous variables," Journal of Econometrics, Elsevier, vol. 228(1), pages 4-26.
    80. Falco J. Bargagli Stoffi & Kenneth De Beckker & Joana E. Maldonado & Kristof De Witte, 2021. "Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy," Papers 2102.04382, arXiv.org.
    81. Ashkan Ertefaie & Nima S. Hejazi & Mark J. van der Laan, 2023. "Nonparametric inverse‐probability‐weighted estimators based on the highly adaptive lasso," Biometrics, The International Biometric Society, vol. 79(2), pages 1029-1041, June.
    82. Guber, Raphael, 2018. "Instrument Validity Tests with Causal Trees: With an Application to the Same-sex Instrument," MEA discussion paper series 201805, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    83. Jushan Bai & Sung Hoon Choi & Yuan Liao, 2019. "Feasible Generalized Least Squares for Panel Data with Cross-sectional and Serial Correlations," Papers 1910.09004, arXiv.org, revised Aug 2020.
    84. Esfandiar Maasoumi & Jianqiu Wang & Zhuo Wang & Ke Wu, 2024. "Identifying factors via automatic debiased machine learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 438-461, April.
    85. Pranjal Rawat, 2024. "A Deep Learning Approach to Heterogeneous Consumer Aesthetics in Retail Fashion," Papers 2405.10498, arXiv.org.
    86. Retsef Levi & Elisabeth Paulson & Georgia Perakis & Emily Zhang, 2024. "Heterogeneous Treatment Effects in Panel Data," Papers 2406.05633, arXiv.org.
    87. Abadie, Alberto & Gu, Jiaying & Shen, Shu, 2024. "Instrumental variable estimation with first-stage heterogeneity," Journal of Econometrics, Elsevier, vol. 240(2).
    88. Phillip Heiler, 2020. "Efficient Covariate Balancing for the Local Average Treatment Effect," Papers 2007.04346, arXiv.org.
    89. Ke Sun & Linglong Kong & Hongtu Zhu & Chengchun Shi, 2024. "ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments," Papers 2408.05342, arXiv.org, revised Dec 2024.
    90. Whitney K. Newey & James M. Robins, 2017. "Cross-fitting and fast remainder rates for semiparametric estimation," CeMMAP working papers 41/17, Institute for Fiscal Studies.
    91. Johannes Jakubik & Stefan Feuerriegel, 2022. "Data‐driven allocation of development aid toward sustainable development goals: Evidence from HIV/AIDS," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2739-2756, June.

  78. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Anti-concentration and honest, adaptive confidence bands," CeMMAP working papers 43/16, Institute for Fiscal Studies.

    Cited by:

    1. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Testing many moment inequalities," CeMMAP working papers CWP42/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Gaussian approximation of suprema of empirical processes," CeMMAP working papers CWP75/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Comparison and anti-concentration bounds for maxima of Gaussian random vectors," CeMMAP working papers 71/13, Institute for Fiscal Studies.
    4. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Ivan Fernandez-Val, 2016. "Conditional quantile processes based on series or many regressors," CeMMAP working papers 46/16, Institute for Fiscal Studies.
    5. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  79. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, 2016. "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.

    Cited by:

    1. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Baumöhl, Eduard & Bouri, Elie & Hoang, Thi-Hong-Van & Hussain Shahzad, Syed Jawad & Výrost, Tomáš, 2022. "Measuring systemic risk in the global banking sector: A cross-quantilogram network approach," Economic Modelling, Elsevier, vol. 109(C).
    3. Torri, Gabriele & Giacometti, Rosella & Tichý, Tomáš, 2021. "Network tail risk estimation in the European banking system," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    4. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2017. "Non-separable Models with High-dimensional Data," Economics and Statistics Working Papers 15-2017, Singapore Management University, School of Economics.
    5. Hossein Alidaee & Eric Auerbach & Michael P. Leung, 2020. "Recovering Network Structure from Aggregated Relational Data using Penalized Regression," Papers 2001.06052, arXiv.org.

  80. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Gaussian approximation of suprema of empirical processes," CeMMAP working papers 41/16, Institute for Fiscal Studies.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "On the asymptotic theory for least squares series: pointwise and uniform results," CeMMAP working papers 73/13, Institute for Fiscal Studies.
    2. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
    3. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, 2016. "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
    4. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    5. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Some New Asymptotic Theory for Least Squares Series: Pointwise and Uniform Results," Papers 1212.0442, arXiv.org, revised Jun 2015.
    6. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers 13/16, Institute for Fiscal Studies.
    7. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Anti-concentration and honest, adaptive confidence bands," CeMMAP working papers 43/16, Institute for Fiscal Studies.
    8. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    9. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors," CeMMAP working papers 76/13, Institute for Fiscal Studies.
    10. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    11. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Central limit theorems and multiplier bootstrap when p is much larger than n," CeMMAP working papers CWP45/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers 57/13, Institute for Fiscal Studies.
    13. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2014. "Uniform post selection inference for LAD regression and other Z-estimation problems," CeMMAP working papers 51/14, Institute for Fiscal Studies.
    14. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  81. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Testing many moment inequalities," CeMMAP working papers 42/16, Institute for Fiscal Studies.

    Cited by:

    1. Andres Aradillas-Lopez & Adam Rosen, 2013. "Inference in ordered response games with complete information," CeMMAP working papers 33/13, Institute for Fiscal Studies.
    2. Kate Ho & Adam M. Rosen, 2015. "Partial Identification in Applied Research: Benefits and Challenges," NBER Working Papers 21641, National Bureau of Economic Research, Inc.
    3. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Andrew Chesher & Adam Rosen, 2016. "Characterizations of identified sets delivered by structural econometric models," CeMMAP working papers CWP44/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Bontemps, Christian & Kumar, Rohit, 2018. "A Geometric Approach to Inference in Set-Identified Entry Games," TSE Working Papers 18-943, Toulouse School of Economics (TSE), revised Mar 2019.
    6. Li, Jia & Liao, Zhipeng, 2020. "Uniform nonparametric inference for time series," Journal of Econometrics, Elsevier, vol. 219(1), pages 38-51.
    7. Andrew Chesher & Adam Rosen, 2019. "Generalized Instrumental Variable Models, Methods, and Applications," CeMMAP working papers CWP41/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Allen, Roy, 2018. "Testing moment inequalities: Selection versus recentering," Economics Letters, Elsevier, vol. 162(C), pages 124-126.
    9. Andrew Chesher & Adam Rosen, 2014. "Generalized instrumental variable models," CeMMAP working papers 04/14, Institute for Fiscal Studies.
    10. Donald W.K. Andrews & Xiaoxia Shi, 2015. "Inference Based on Many Conditional Moment Inequalities," Cowles Foundation Discussion Papers 2010, Cowles Foundation for Research in Economics, Yale University.
    11. Baris Ata & Alexandre Belloni & Ozan Candogan, 2018. "Latent Agents in Networks: Estimation and Targeting," Papers 1808.04878, arXiv.org, revised Jan 2022.
    12. Chen, Likai & Wang, Weining & Wu, Wei Biao, 2019. "Inference of Break-Points in High-Dimensional Time Series," IRTG 1792 Discussion Papers 2019-013, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    13. Chang, Jinyuan & Qiu, Yumou & Yao, Qiwei & Zou, Tao, 2018. "Confidence regions for entries of a large precision matrix," Journal of Econometrics, Elsevier, vol. 206(1), pages 57-82.
    14. Chang, Jinyuan & Qiu, Yumou & Yao, Qiwei & Zou, Tao, 2018. "Confidence regions for entries of a large precision matrix," LSE Research Online Documents on Economics 87513, London School of Economics and Political Science, LSE Library.
    15. Christian Bontemps & Thierry Magnac, 2017. "Set Identification, Moment Restrictions, and Inference," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 103-129, September.
    16. Matias D. Cattaneo & Xinwei Ma & Yusufcan Masatlioglu & Elchin Suleymanov, 2020. "A Random Attention Model," Journal of Political Economy, University of Chicago Press, vol. 128(7), pages 2796-2836.
    17. Guber, Raphael, 2018. "Instrument Validity Tests with Causal Trees: With an Application to the Same-sex Instrument," MEA discussion paper series 201805, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.

  82. Mingli Chen & Victor Chernozhukov & Iv'an Fern'andez-Val & Blaise Melly, 2016. "Counterfactual: An R Package for Counterfactual Analysis," Papers 1610.07894, arXiv.org.

    Cited by:

    1. Qiu, Wanling & Rudkin, Simon & Sharma, Abhijit, 2017. "An Analysis of the Impact of Low Cost Airlines on Tourist Stay Duration and Expenditures," MPRA Paper 81428, University Library of Munich, Germany.
    2. Delprato, Marcos & Chudgar, Amita, 2018. "Factors associated with private-public school performance: Analysis of TALIS-PISA link data," International Journal of Educational Development, Elsevier, vol. 61(C), pages 155-172.

  83. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers 49/16, Institute for Fiscal Studies.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers 13/16, Institute for Fiscal Studies.
    2. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Christian Hansen & Damian Kozbur & Sanjog Misra, 2016. "Targeted undersmoothing," ECON - Working Papers 282, Department of Economics - University of Zurich, revised Apr 2018.
    4. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    5. Christian Hansen & Yuan Liao, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," Papers 1611.09420, arXiv.org, revised Dec 2016.
    6. Alexandrov, Alexei & Pittman, Russell & Ukhaneva, Olga, 2018. "Pricing of Complements in the U.S. freight railroads: Cournot versus Coase," MPRA Paper 86279, University Library of Munich, Germany.
    7. Bernard Koch & Tim Sainburg & Pablo Geraldo & Song Jiang & Yizhou Sun & Jacob Gates Foster, 2021. "A Primer on Deep Learning for Causal Inference," Papers 2110.04442, arXiv.org, revised Nov 2023.
    8. Susan Athey & Stefan Wager, 2017. "Policy Learning with Observational Data," Papers 1702.02896, arXiv.org, revised Sep 2020.
    9. Damian Kozbur, 2015. "Testing-Based Forward Model Selection," ECON - Working Papers 283, Department of Economics - University of Zurich, revised Apr 2018.
    10. Dmitry Arkhangelsky & Guido Imbens, 2018. "The Role of the Propensity Score in Fixed Effect Models," NBER Working Papers 24814, National Bureau of Economic Research, Inc.
    11. Alexandrov, Alexei & Pittman, Russell & Ukhaneva, Olga, 2017. "Royalty stacking in the U.S. freight railroads: Cournot vs. Coase," MPRA Paper 78249, University Library of Munich, Germany.
    12. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
    13. Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
    14. Zhengyuan Zhou & Susan Athey & Stefan Wager, 2018. "Offline Multi-Action Policy Learning: Generalization and Optimization," Papers 1810.04778, arXiv.org, revised Nov 2018.
    15. Kueck, Jannis & Luo, Ye & Spindler, Martin & Wang, Zigan, 2023. "Estimation and inference of treatment effects with L2-boosting in high-dimensional settings," Journal of Econometrics, Elsevier, vol. 234(2), pages 714-731.
    16. Lauren Cappiello & Zhiwei Zhang & Changyu Shen & Neel M. Butala & Xinping Cui & Robert W. Yeh, 2021. "Adjusting for population differences using machine learning methods," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 750-769, June.
    17. Yan-Yu Chiou & Mei-Yuan Chen & Jau-er Chen, 2017. "Nonparametric Regression with Multiple Thresholds: Estimation and Inference," Papers 1705.09418, arXiv.org, revised Feb 2018.
    18. Jannis Kueck & Ye Luo & Martin Spindler & Zigan Wang, 2017. "Estimation and Inference of Treatment Effects with $L_2$-Boosting in High-Dimensional Settings," Papers 1801.00364, arXiv.org, revised Jul 2021.
    19. Jiaming Mao & Zhesheng Zheng, 2020. "Structural Regularization," Papers 2004.12601, arXiv.org, revised Jun 2020.
    20. Jiaming Mao & Jingzhi Xu, 2020. "Ensemble Learning with Statistical and Structural Models," Papers 2006.05308, arXiv.org.
    21. Mochen Yang & Edward McFowland & Gordon Burtch & Gediminas Adomavicius, 2022. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 138-155, October.
    22. Jean-Pierre Dubé & Sanjog Misra, 2017. "Personalized Pricing and Consumer Welfare," NBER Working Papers 23775, National Bureau of Economic Research, Inc.
    23. Chiou, Yan-Yu & Chen, Mei-Yuan & Chen, Jau-er, 2018. "Nonparametric regression with multiple thresholds: Estimation and inference," Journal of Econometrics, Elsevier, vol. 206(2), pages 472-514.
    24. Daniel L. Chen & Markus Loecher, 2022. "Mood and the Malleability of Moral Reasoning: The Impact of Irrelevant Factors on Judicial Decisions," Working Papers hal-03864854, HAL.
    25. Victor Chernozhukov & Vira Semenova, 2018. "Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions," CeMMAP working papers CWP40/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    26. Ryo Aruga & Keiichi Goshima & Takashi Chiba, 2022. "CO2 Emissions and Corporate Performance: Japan's Evidence with Double Machine Learning," IMES Discussion Paper Series 22-E-01, Institute for Monetary and Economic Studies, Bank of Japan.
    27. Lester Mackey & Vasilis Syrgkanis & Ilias Zadik, 2017. "Orthogonal Machine Learning: Power and Limitations," Papers 1711.00342, arXiv.org, revised Aug 2018.
    28. Susan Athey & Guido Imbens, 2016. "The State of Applied Econometrics - Causality and Policy Evaluation," Papers 1607.00699, arXiv.org.
    29. Jushan Bai & Sung Hoon Choi & Yuan Liao, 2019. "Feasible Generalized Least Squares for Panel Data with Cross-sectional and Serial Correlations," Papers 1910.09004, arXiv.org, revised Aug 2020.
    30. Crane-Droesch, Andrew, 2017. "Semiparametric Panel Data Using Neural Networks," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258128, Agricultural and Applied Economics Association.
    31. Thai T. Pham & Yuanyuan Shen, 2017. "A Deep Causal Inference Approach to Measuring the Effects of Forming Group Loans in Online Non-profit Microfinance Platform," Papers 1706.02795, arXiv.org.

  84. Michael Lipsitz & Alexandre Belloni & Victor Chernozhukov & Iv'an Fern'andez-Val, 2016. "quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile Regression," Papers 1610.08329, arXiv.org.

    Cited by:

    1. Bruneel-Zupanc, Christophe Alain, 2021. "Discrete-Continuous Dynamic Choice Models: Identification and Conditional Choice Probability Estimation," TSE Working Papers 21-1185, Toulouse School of Economics (TSE).
    2. Clarke, Damian & Llorca-Jaña, Manuel & Pailañir, Daniel, 2021. "The Use of Quantile Methods in Economic History," IZA Discussion Papers 14659, Institute of Labor Economics (IZA).
    3. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Ivan Fernandez-Val, 2016. "Conditional quantile processes based on series or many regressors," CeMMAP working papers 46/16, Institute for Fiscal Studies.

  85. Victor Chernozhukov & Christian Hansen & Yuan Liao, 2015. "A lava attack on the recovery of sums of dense and sparse signals," Papers 1502.03155, arXiv.org, revised Mar 2015.

    Cited by:

    1. Victor Chernozhukov & Christian Hansen & Yuan Liao, 2015. "A lava attack on the recovery of sums of dense and sparse signals," CeMMAP working papers CWP56/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  86. Patrick Bajari & Victor Chernozhukov & Han Hong & Denis Nekipelov, 2015. "Identification and Efficient Semiparametric Estimation of a Dynamic Discrete Game," NBER Working Papers 21125, National Bureau of Economic Research, Inc.

    Cited by:

    1. Cook, Jonathan A. & Lin, C.-Y. Cynthia, 2015. "Wind Turbine Shutdowns and Upgrades in Denmark: Timing Decisions and the Impact of Government Policy," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 204960, Agricultural and Applied Economics Association.
    2. Jackson Bunting, 2022. "Continuous permanent unobserved heterogeneity in dynamic discrete choice models," Papers 2202.03960, arXiv.org, revised Feb 2024.
    3. Taiga Tsubota, 2021. "Identifying Dynamic Discrete Choice Models with Hyperbolic Discounting," Papers 2111.10721, arXiv.org, revised Oct 2024.
    4. Evgeny Yakovlev, 2016. "Demand for Alcohol Consumption and Implication for Mortality: Evidence from Russia," Working Papers w0221, Center for Economic and Financial Research (CEFIR).
    5. Jaap H. Abbring & Jeffrey R. Campbell & Jan Tilly & Nan Yang, 2018. "Very Simple Markov-Perfect Industry Dynamics: Empirics," Working Paper Series WP-2018-17, Federal Reserve Bank of Chicago.
    6. Abbring, Jaap & Daljord, Øystein, 2016. "Identifying the Discount Factor in Dynamic Discrete Choice Models," CEPR Discussion Papers 11133, C.E.P.R. Discussion Papers.
    7. Song Yao & Carl F. Mela, 2011. "A Dynamic Model of Sponsored Search Advertising," Marketing Science, INFORMS, vol. 30(3), pages 447-468, 05-06.
    8. Haitian Xie, 2020. "Efficient and Robust Estimation of the Generalized LATE Model," Papers 2001.06746, arXiv.org, revised Feb 2022.
    9. Abbring, Jaap & Campbell, J.R. & Tilly, J. & Yang, N., 2018. "Very Simple Markov-Perfect Industry Dynamics (revision of 2017-021) : Empirics," Other publications TiSEM 3a12f099-900b-44ac-b692-a, Tilburg University, School of Economics and Management.
    10. Zhaohui (Zoey) Jiang & Yan Huang & Damian R. Beil, 2022. "The Role of Feedback in Dynamic Crowdsourcing Contests: A Structural Empirical Analysis," Management Science, INFORMS, vol. 68(7), pages 4858-4877, July.
    11. A. Ronald Gallant & Han Hong & Ahmed Khwaja, 2018. "The Dynamic Spillovers of Entry: An Application to the Generic Drug Industry," Management Science, INFORMS, vol. 64(3), pages 1189-1211, March.
    12. Rojas Valdes, Ruben I. & Lin Lawell, C.-Y. Cynthia & Taylor, J. Edward, 2017. "The Dynamic Migration Game: A Structural Econometric Model and Application to Rural Mexico," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 259184, Agricultural and Applied Economics Association.
    13. Lawell, Cynthia Lin & Yi, Fujin & Thome, Karen E, 2017. "The Effects of Subsidies and Mandates: A Dynamic Model of the Ethanol Industry," Institute of Transportation Studies, Working Paper Series qt73n0t4pv, Institute of Transportation Studies, UC Davis.
    14. Kheiravar, Khaled H, 2019. "Economic and Econometric Analyses of the World Petroleum Industry, Energy Subsidies, and Air Pollution," Institute of Transportation Studies, Working Paper Series qt3gj151w9, Institute of Transportation Studies, UC Davis.

  87. Victor Chernozhukov & Whitney K. Newey & Andres Santos, 2015. "Constrained conditional moment restriction models," CeMMAP working papers 59/15, Institute for Fiscal Studies.

    Cited by:

    1. Zheng Fang & Juwon Seo, 2021. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Econometrica, Econometric Society, vol. 89(5), pages 2439-2458, September.
    2. Vira Semenova, 2023. "Aggregated Intersection Bounds and Aggregated Minimax Values," Papers 2303.00982, arXiv.org, revised Jun 2024.
    3. Xiaohong Chen & Yin Jia Jeff Qiu, 2016. "Methods for Nonparametric and Semiparametric Regressions with Endogeneity: A Gentle Guide," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 259-290, October.
    4. Chen, Qihui, 2021. "Robust and optimal estimation for partially linear instrumental variables models with partial identification," Journal of Econometrics, Elsevier, vol. 221(2), pages 368-380.
    5. Babii, Andrii & Kumar, Rohit, 2023. "Isotonic regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 234(2), pages 371-393.
    6. Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
    7. Kline, Patrick & Walters, Christopher, 2019. "Audits as Evidence: Experiments, Ensembles, and Enforcement," Institute for Research on Labor and Employment, Working Paper Series qt3z72m9kn, Institute of Industrial Relations, UC Berkeley.
    8. Roy Allen & John Rehbeck, 2020. "Identification of Random Coefficient Latent Utility Models," Papers 2003.00276, arXiv.org.
    9. Chen, Xiaohong & Pouzo, Demian & Powell, James L., 2019. "Penalized sieve GEL for weighted average derivatives of nonparametric quantile IV regressions," Journal of Econometrics, Elsevier, vol. 213(1), pages 30-53.
    10. Denis Chetverikov & Daniel Wilhelm, 2017. "Nonparametric instrumental variable estimation under monotonicity," CeMMAP working papers CWP14/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Isaac Loh, 2024. "Inference under partial identification with minimax test statistics," Papers 2401.13057, arXiv.org, revised Apr 2024.
    12. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Ivan Fernandez-Val, 2016. "Conditional quantile processes based on series or many regressors," CeMMAP working papers 46/16, Institute for Fiscal Studies.
    13. Zheng Fang & Andres Santos & Azeem M. Shaikh & Alexander Torgovitsky, 2020. "Inference for Large-Scale Linear Systems with Known Coefficients," Papers 2009.08568, arXiv.org, revised Sep 2021.
    14. Lee, Y-Y. & Bhattacharya, D., 2018. "Applied Welfare Analysis for Discrete Choice with Interval-data on Income," Cambridge Working Papers in Economics 1882, Faculty of Economics, University of Cambridge.
    15. Christoph Breunig & Xiaohong Chen, 2020. "Adaptive, Rate-Optimal Hypothesis Testing in Nonparametric IV Models," Cowles Foundation Discussion Papers 2238R, Cowles Foundation for Research in Economics, Yale University, revised Dec 2021.
    16. Zheng Fang, 2021. "A Unifying Framework for Testing Shape Restrictions," Papers 2107.12494, arXiv.org, revised Aug 2021.
    17. Isaiah Andrews & Jonathan Roth & Ariel Pakes, 2023. "Inference for Linear Conditional Moment Inequalities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(6), pages 2763-2791.
    18. Matias D. Cattaneo & Xinwei Ma & Yusufcan Masatlioglu & Elchin Suleymanov, 2020. "A Random Attention Model," Journal of Political Economy, University of Chicago Press, vol. 128(7), pages 2796-2836.
    19. Christoph Breunig & Xiaohong Chen, 2020. "Adaptive, Rate-Optimal Hypothesis Testing in Nonparametric IV Models," Papers 2006.09587, arXiv.org, revised Nov 2024.
    20. Patrick Kline & Christopher Walters, 2021. "Reasonable Doubt: Experimental Detection of Job‐Level Employment Discrimination," Econometrica, Econometric Society, vol. 89(2), pages 765-792, March.
    21. Ivan Korolev, 2018. "A Consistent Heteroskedasticity Robust LM Type Specification Test for Semiparametric Models," Papers 1810.07620, arXiv.org, revised Nov 2019.
    22. Zheng Fang & Juwon Seo, 2019. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Papers 1910.07689, arXiv.org, revised Sep 2021.
    23. Matias D. Cattaneo & Max H. Farrell & Yingjie Feng, 2018. "Large Sample Properties of Partitioning-Based Series Estimators," Papers 1804.04916, arXiv.org, revised Jun 2019.
    24. Giovanni Compiani, 2022. "Market counterfactuals and the specification of multiproduct demand: A nonparametric approach," Quantitative Economics, Econometric Society, vol. 13(2), pages 545-591, May.
    25. David M. Ritzwoller & Vasilis Syrgkanis, 2024. "Simultaneous Inference for Local Structural Parameters with Random Forests," Papers 2405.07860, arXiv.org, revised Sep 2024.

  88. Victor Chernozhukov & Ivan Fernandez-Val & Ye Luo, 2015. "The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages," Papers 1512.05635, arXiv.org, revised May 2018.

    Cited by:

    1. Daniel Goller, 2020. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Papers 2008.07165, arXiv.org.
    2. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment," DETU Working Papers 1804, Department of Economics, Temple University.
    3. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods," Papers 1803.09015, arXiv.org, revised Dec 2020.
    4. Victor Chernozhukov & Mert Demirer & Esther Duflo & Ivan Fernandez-Val, 2017. "Generic machine learning inference on heterogenous treatment effects in randomized experiments," CeMMAP working papers CWP61/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
    6. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    7. Diego Marino Fages, 2023. "Migration and trust: Evidence on assimilation from internal migrants," Discussion Papers 2023-08, Nottingham Interdisciplinary Centre for Economic and Political Research (NICEP).
    8. Sallin, Aurelién, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Economics Working Paper Series 2109, University of St. Gallen, School of Economics and Political Science.
    9. Christian Hansen & Damian Kozbur & Sanjog Misra, 2016. "Targeted undersmoothing," ECON - Working Papers 282, Department of Economics - University of Zurich, revised Apr 2018.
    10. Wunsch, Conny & Strittmatter, Anthony, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," CEPR Discussion Papers 15840, C.E.P.R. Discussion Papers.
    11. Florent Dubois & Christophe Muller, 2022. "Residential segregation matters to racial income gaps: Evidence from South Africa," AMSE Working Papers 2205, Aix-Marseille School of Economics, France.
    12. Knaus, Michael C., 2020. "Double Machine Learning Based Program Evaluation under Unconfoundedness," IZA Discussion Papers 13051, Institute of Labor Economics (IZA).
    13. Sookyo Jeong & Hongseok Namkoong, 2020. "Assessing External Validity Over Worst-case Subpopulations," Papers 2007.02411, arXiv.org, revised Feb 2022.
    14. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Apr 2024.
    15. Florent Dubois & Christophe Muller, 2022. "Residential segregation matters to racial income gaps," Working Papers hal-03622711, HAL.
    16. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iv'an Fern'andez-Val, 2017. "Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," Papers 1712.04802, arXiv.org, revised Oct 2023.
    17. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey, 2017. "Nonseparable multinomial choice models in cross-section and panel data," CeMMAP working papers 33/17, Institute for Fiscal Studies.
    18. Strittmatter, Anthony, 2019. "What is the Value Added by using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203499, Verein für Socialpolitik / German Economic Association.
    19. Zheng Fang, 2021. "A Unifying Framework for Testing Shape Restrictions," Papers 2107.12494, arXiv.org, revised Aug 2021.
    20. Florent Dubois & Christophe Muller, 2020. "The Contribution of Residential Segregation to Racial Income Gaps: Evidence from South Africa," Working Papers halshs-02944720, HAL.
    21. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," CESifo Working Paper Series 9037, CESifo.
    22. Posel, Dorrit & Oyenubi, Adeola, 2023. "Heterogeneous gender gaps in mental wellbeing: Do women with low economic status face the biggest gender gaps?," Social Science & Medicine, Elsevier, vol. 332(C).
    23. Martin Kroczek & Philipp Kugler, 2022. "Heterogeneous Effects of Monetary and Non-Monetary Job Characteristics on Job Attractiveness in Nursing," IAW Discussion Papers 139, Institut für Angewandte Wirtschaftsforschung (IAW).
    24. Lopez Garcia, Italo & Luoto, Jill E. & Aboud, Frances E. & Fernald, Lia C.H., 2023. "Group Meetings and Boosters to Sustain Early Impacts on Child Development: Experimental Evidence from Kenya," IZA Discussion Papers 16392, Institute of Labor Economics (IZA).
    25. Tsionas, Mike, 2022. "Efficiency estimation using probabilistic regression trees with an application to Chilean manufacturing industries," International Journal of Production Economics, Elsevier, vol. 249(C).
    26. Victor Chernozhukov & Iván Fernández‐Val & Ye Luo, 2018. "The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages," Econometrica, Econometric Society, vol. 86(6), pages 1911-1938, November.
    27. Kroczek, Martin & Kugler, Philipp, 2022. "Heterogeneous Effects of Monetary and Non-Monetary Job Characteristics on Job Attractiveness in Nursing," VfS Annual Conference 2022 (Basel): Big Data in Economics 264108, Verein für Socialpolitik / German Economic Association.
    28. Michael Zimmert & Michael Lechner, 2019. "Nonparametric estimation of causal heterogeneity under high-dimensional confounding," Papers 1908.08779, arXiv.org.
    29. Daniel Jacob, 2019. "Group Average Treatment Effects for Observational Studies," Papers 1911.02688, arXiv.org, revised Mar 2020.
    30. Olivier Deschenes & Christopher Malloy & Gavin G. McDonald, 2023. "Causal Effects of Renewable Portfolio Standards on Renewable Investments and Generation: The Role of Heterogeneity and Dynamics," NBER Working Papers 31568, National Bureau of Economic Research, Inc.
    31. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
    32. Yuehao Bai, 2022. "Optimality of Matched-Pair Designs in Randomized Controlled Trials," Papers 2206.07845, arXiv.org.
    33. Laub, Natalie & Boockmann, Bernhard & Kroczek, Martin, 2023. "Tightening Access to Early Retirement: Who Can Adapt?," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277625, Verein für Socialpolitik / German Economic Association.
    34. Jacob, Daniel & Härdle, Wolfgang Karl & Lessmann, Stefan, 2019. "Group Average Treatment Effects for Observational Studies," IRTG 1792 Discussion Papers 2019-028, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    35. Pullabhotla, Hemant K. & Souza, Mateus, 2022. "Air pollution from agricultural fires increases hypertension risk," Journal of Environmental Economics and Management, Elsevier, vol. 115(C).
    36. Bilancini, Ennio & Boncinelli, Leonardo & Di Paolo, Roberto & Menicagli, Dario & Pizziol, Veronica & Ricciardi, Emiliano & Serti, Francesco, 2022. "Prosocial behavior in emergencies: Evidence from blood donors recruitment and retention during the COVID-19 pandemic," Social Science & Medicine, Elsevier, vol. 314(C).
    37. Laura Liu & Alexandre Poirier & Ji-Liang Shiu, 2021. "Identification and Estimation of Partial Effects in Nonlinear Semiparametric Panel Models," Papers 2105.12891, arXiv.org, revised Jul 2024.
    38. Bernhard Boockmann & Martin Kroczek & Natalie Laub, 2023. "Tightening access to early retirement: who can adapt?," IAW Discussion Papers 142, Institut für Angewandte Wirtschaftsforschung (IAW).
    39. Aur'elien Sallin, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Papers 2110.08807, arXiv.org, revised Feb 2022.
    40. Yuri Fonseca & Marcelo Medeiros & Gabriel Vasconcelos & Alvaro Veiga, 2018. "BooST: Boosting Smooth Trees for Partial Effect Estimation in Nonlinear Regressions," Papers 1808.03698, arXiv.org, revised Jul 2020.
    41. Kai Feng & Han Hong, 2024. "Statistical Inference of Optimal Allocations I: Regularities and their Implications," Papers 2403.18248, arXiv.org, revised Apr 2024.

  89. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," Papers 1501.03185, arXiv.org.

    Cited by:

    1. Zhong, Wei & Gao, Yang & Zhou, Wei & Fan, Qingliang, 2021. "Endogenous treatment effect estimation using high-dimensional instruments and double selection," Statistics & Probability Letters, Elsevier, vol. 169(C).
    2. Bottmer, Lea & Croux, Christophe & Wilms, Ines, 2022. "Sparse regression for large data sets with outliers," European Journal of Operational Research, Elsevier, vol. 297(2), pages 782-794.
    3. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    4. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers 13/16, Institute for Fiscal Studies.
    5. Franz Huber & Alan Ponce & Francesco Rentocchini & Thomas Wainwright, 2020. "The Wealth of (Open Data) Nations? Examining the interplay of open government data and country-level institutions for entrepreneurial activity at the country-level," SEEDS Working Papers 1120, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Nov 2020.
    6. Peter C. B. Phillips & Zhentao Shi, 2021. "Boosting: Why You Can Use The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 521-570, May.
    7. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2024. "ddml: Double/debiased machine learning in Stata," Stata Journal, StataCorp LP, vol. 24(1), pages 3-45, March.
    8. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    9. Shukla, Pallavi & Pullabhotla, Hemant K. & Baylis, Kathy, 2022. "Trouble with zero: The limits of subsidizing technology adoption," Journal of Development Economics, Elsevier, vol. 158(C).
    10. Krüger, Jens J. & Rhiel, Mathias, 2016. "Determinants of ICT infrastructure: A cross-country statistical analysis," Darmstadt Discussion Papers in Economics 228, Darmstadt University of Technology, Department of Law and Economics.
    11. Duso, Tomaso & Michelsen, Claus & Schaefer, Maximilian & Tran, Kevin, 2021. "Airbnb and Rental Markets: Evidence from Berlin," CEPR Discussion Papers 16150, C.E.P.R. Discussion Papers.
    12. Godzinski, Alexandre & Suarez Castillo, Milena, 2021. "Disentangling the effects of air pollutants with many instruments," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).
    13. Giuseppe de Luca & Jan Magnus & Franco Peracchi, 2017. "Weighted-Average Least Squares Estimation of Generalized Linear Models," Tinbergen Institute Discussion Papers 17-029/III, Tinbergen Institute.
    14. Bilgin, Rumeysa, 2023. "The Selection Of Control Variables In Capital Structure Research With Machine Learning," SocArXiv e26qf, Center for Open Science.
    15. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    16. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    17. Woodruff, Christopher & Menzel, Andreas, 2019. "Gender Wage Gaps and Worker Mobility: Evidence from the Garment Sector in Bangladesh," CEPR Discussion Papers 13577, C.E.P.R. Discussion Papers.
    18. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2019. "lassopack: Model selection and prediction with regularized regression in Stata," Papers 1901.05397, arXiv.org.
    19. Gal Amedi, 2023. "The Determinants of the Transit Accessibility Premium," Bank of Israel Working Papers 2023.12, Bank of Israel.
    20. Cameron, Lisa & Gertler, Paul & Shah, Manisha & Alzua, Maria Laura & Martinez, Sebastian & Patil, Sumeet, 2022. "The dirty business of eliminating open defecation: The effect of village sanitation on child height from field experiments in four countries," Journal of Development Economics, Elsevier, vol. 159(C).
    21. Alessandro V. M. Oliveira & Bruno F. Oliveira & Moises D. Vassallo, 2024. "Airport service quality perception and flight delays: examining the influence of psychosituational latent traits of respondents in passenger satisfaction surveys," Papers 2401.02139, arXiv.org.
    22. Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers 57/13, Institute for Fiscal Studies.
    23. Aglasan, Serkan & Goodwin, Barry K. & Rejesus, Roderick, 2020. "Genetically Modified Rootworm-Resistant Corn, Risk, and Weather: Evidence from High Dimensional Methods," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 305181, Agricultural and Applied Economics Association.
    24. Santos, Luca J. & Oliveira, Alessandro V.M. & Aldrighi, Dante Mendes, 2021. "Testing the differentiated impact of the COVID-19 pandemic on air travel demand considering social inclusion," Journal of Air Transport Management, Elsevier, vol. 94(C).
    25. Zhaonan Qu & Yongchan Kwon, 2024. "Distributionally Robust Instrumental Variables Estimation," Papers 2410.15634, arXiv.org, revised Dec 2024.
    26. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," Papers 1501.03185, arXiv.org.
    27. Seojeong Lee & Youngki Shin, 2021. "Complete subset averaging with many instruments," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 290-314.
    28. Bendini,Maria Magdalena & Dinarte Diaz,Lelys Ileana, 2020. "Does Maternal Depression Undermine Childhood Cognitive Development? Evidence from the Young Lives Survey in Peru," Policy Research Working Paper Series 9479, The World Bank.
    29. Peter C.B. Phillips & Zhentao Shi, 2019. "Boosting the Hodrick-Prescott Filter," Cowles Foundation Discussion Papers 2192, Cowles Foundation for Research in Economics, Yale University.
    30. Michael Danquah & Abdul Malik Iddrisu & Ernest Owusu Boakye & Solomon Owusu, 2021. "Do gender wage differences within households influence women's empowerment and welfare?: Evidence from Ghana," WIDER Working Paper Series wp-2021-40, World Institute for Development Economic Research (UNU-WIDER).
    31. Kaila, Heidi & Azad, Abul, 2023. "The effects of crime and violence on food insecurity and consumption in Nigeria," Food Policy, Elsevier, vol. 115(C).
    32. Frank Windmeijer & Helmut Farbmacher & Neil Davies & George Davey Smith, 2016. "On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments," Bristol Economics Discussion Papers 16/674, School of Economics, University of Bristol, UK, revised 08 Aug 2017.
    33. Ruf, Daniel, 2017. "Agglomeration Effects and Liquidity Gradients in Local Rental Housing Markets," Working Papers on Finance 1702, University of St. Gallen, School of Finance.
    34. Li, Jing & Li, Liyao & Liu, Shimeng, 2022. "Attenuation of agglomeration economies: Evidence from the universe of Chinese manufacturing firms," Journal of Urban Economics, Elsevier, vol. 130(C).
    35. Susan Athey & Guido Imbens, 2016. "The Econometrics of Randomized Experiments," Papers 1607.00698, arXiv.org.
    36. Ofori, Isaac K. & Quaidoo, Christopher & Ofori, Pamela E., 2021. "What Drives Financial Sector Development in Africa? Insights from Machine Learning," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue forthcomi.
    37. Max-Sebastian Dov`i, 2021. "Inference on the New Keynesian Phillips Curve with Very Many Instrumental Variables," Papers 2101.09543, arXiv.org, revised Mar 2021.
    38. Marianne Bl'ehaut & Xavier D'Haultfoeuille & J'er'emy L'Hour & Alexandre B. Tsybakov, 2020. "An alternative to synthetic control for models with many covariates under sparsity," Papers 2005.12225, arXiv.org, revised Jun 2021.
    39. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2017. "Non-separable Models with High-dimensional Data," Economics and Statistics Working Papers 15-2017, Singapore Management University, School of Economics.
    40. Pettersson-Lidbom, Per, 2022. "Exit, Voice and Political Change: Evidence from Swedish Mass Migration to the United States. A Comment on Karadja and Prawitz (Journal of Political Economy, 2019)," Journal of Comments and Replications in Economics (JCRE), ZBW - Leibniz Information Centre for Economics, vol. 1(2022-3), pages 1-13.
    41. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2020. "Ill-posed estimation in high-dimensional models with instrumental variables," Journal of Econometrics, Elsevier, vol. 219(1), pages 171-200.
    42. Samarth Gupta, 2023. "Model-Selection Inference for Causal Impact of Clusters and Collaboration on MSMEs in India," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(3), pages 641-662, September.
    43. Wang, Steven Shuye & Xu, Kuan & Zhang, Hao, 2019. "A microstructure study of circuit breakers in the Chinese stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    44. Madina Kurmangaliyeva & Matteo Sostero, 2022. "Walking while Black :Racial Gaps in Hit-and-Run Cases," Working Papers ECARES 2022-08, ULB -- Universite Libre de Bruxelles.
    45. Natalia Garbiras-Díaz & Mateo Montenegro, 2022. "All Eyes on Them: A Field Experiment on Citizen Oversight and Electoral Integrity," American Economic Review, American Economic Association, vol. 112(8), pages 2631-2668, August.
    46. Alessandro V. M. Oliveira & Thiago Caliari & Rodolfo R. Narcizo, 2024. "An empirical model of fleet modernization: on the relationship between market concentration and innovation adoption in the Brazilian airline industry," Papers 2401.06876, arXiv.org.
    47. Lai Xinglin, 2021. "Modelling hetegeneous treatment effects by quantitle local polynomial decision tree and forest," Papers 2111.15320, arXiv.org, revised Mar 2022.
    48. Joshua Angrist & Brigham Frandsen, 2019. "Machine Labor," NBER Working Papers 26584, National Bureau of Economic Research, Inc.
    49. Guo, Zijian & Kang, Hyunseung & Cai, T. Tony & Small, Dylan S., 2018. "Testing endogeneity with high dimensional covariates," Journal of Econometrics, Elsevier, vol. 207(1), pages 175-187.
    50. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    51. Sophie Brana & Dalila Chenaf-Nicet & Delphine Lahet, 2023. "Drivers of cross-border bank claims: The role of foreign-owned banks in emerging countries," Working Papers 2023.06, International Network for Economic Research - INFER.
    52. James M. Carson & Cameron M. Ellis & Robert E. Hoyt & Krzysztof Ostaszewski, 2020. "Sunk Costs and Screening: Two‐Part Tariffs in Life Insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 87(3), pages 689-718, September.
    53. Sander Gerritsen & Mark Kattenberg & Sonny Kuijpers, 2019. "The impact of age at arrival on education and mental health," CPB Discussion Paper 389, CPB Netherlands Bureau for Economic Policy Analysis.
    54. Pushan Dutt & Ilia Tsetlin, 2021. "Income distribution and economic development: Insights from machine learning," Economics and Politics, Wiley Blackwell, vol. 33(1), pages 1-36, March.
    55. Doğan, Osman & Taşpınar, Süleyman & Bera, Anil K., 2021. "A Bayesian robust chi-squared test for testing simple hypotheses," Journal of Econometrics, Elsevier, vol. 222(2), pages 933-958.
    56. Tomaso Duso & Claus Michelsen & Maximilian Schäfer & Kevin Ducbao Tran, 2020. "Airbnb and Rents: Evidence from Berlin," Discussion Papers of DIW Berlin 1890, DIW Berlin, German Institute for Economic Research.
    57. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    58. Brito, Igor R.S. & Oliveira, Alessandro V.M. & Dresner, Martin E., 2021. "An econometric study of the effects of airport privatization on airfares in Brazil," Transport Policy, Elsevier, vol. 114(C), pages 338-349.
    59. Duso, Tomaso & Michelsen, Claus & Schaefer, Maximilian & Tran, Kevin Ducbao, 2024. "Airbnb and rental markets: Evidence from Berlin," Regional Science and Urban Economics, Elsevier, vol. 106(C).
    60. Seojeong Lee & Youngki Shin, 2018. "Optimal Estimation with Complete Subsets of Instruments," Department of Economics Working Papers 2018-15, McMaster University.
    61. Eufrásio, Ana Beatriz R. & Eller, Rogéria A.G. & Oliveira, Alessandro V.M., 2021. "Are on-time performance statistics worthless? An empirical study of the flight scheduling strategies of Brazilian airlines," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    62. Bai Huang & Tae-Hwy Lee & Aman Ullah, 2017. "A combined estimator of regression models with measurement errors," Indian Economic Review, Springer, vol. 52(1), pages 73-91, December.
    63. Daniel Brunstein & Georges Casamatta & Sauveur Giannoni, 2023. "Hétérogénéité de l'impact du développement d'Airbnb sur le marché immobilier en Corse," Post-Print hal-04122192, HAL.
    64. Liu, Yan & Chen, Minjie & Yu, Jianyu & Wang, Xiaobing, 2024. "Being a happy farmer: Technology adoption and subjective well-being," Journal of Economic Behavior & Organization, Elsevier, vol. 221(C), pages 385-405.
    65. Belloni, Alexandre & Hansen, Christian & Newey, Whitney, 2022. "High-dimensional linear models with many endogenous variables," Journal of Econometrics, Elsevier, vol. 228(1), pages 4-26.
    66. Cocker Liu & Adam Nowak & Patrick Smith, 2017. "Some Remarks on Real Estate Pricing," Working Papers 17-20, Department of Economics, West Virginia University.
    67. Guber, Raphael, 2018. "Instrument Validity Tests with Causal Trees: With an Application to the Same-sex Instrument," MEA discussion paper series 201805, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    68. Koen van Ruijven & Joep Tijm, 2021. "Housing Market Effects of a Railroad Tunneling: Evidence from a quasi-experiment," CPB Discussion Paper 423, CPB Netherlands Bureau for Economic Policy Analysis.
    69. Kyle Myers & Wei Yang Tham, 2023. "Money, Time, and Grant Design," Papers 2312.06479, arXiv.org.
    70. Rishabh Tyagi & Peter Eibich & Vegard Skirbekk, 2024. "Gender norms and partnership dissolution following involuntary job loss in Germany," MPIDR Working Papers WP-2024-027, Max Planck Institute for Demographic Research, Rostock, Germany.
    71. Oliveira, Alessandro V.M. & Oliveira, Bruno F. & Vassallo, Moisés D., 2023. "Airport service quality perception and flight delays: Examining the influence of psychosituational latent traits of respondents in passenger satisfaction surveys," Research in Transportation Economics, Elsevier, vol. 102(C).

  90. Guillaume Carlier & Victor Chernozhukov & Alfred Galichon, 2015. "Vector quantile regression: an optimal transport approach," CeMMAP working papers 58/15, Institute for Fiscal Studies.

    Cited by:

    1. Florian Gunsilius & Susanne M. Schennach, 2017. "A nonlinear principal component decomposition," CeMMAP working papers CWP16/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Dmitry Arkhangelsky, 2019. "Dealing with a Technological Bias: The Difference-in-Difference Approach," Working Papers wp2019_1903, CEMFI.
    3. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," SciencePo Working papers Main hal-03936221, HAL.
    4. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    5. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Ozdemir, Huseyin & Wohar, Mark E., 2020. "Transmission of US and EU Economic Policy Uncertainty Shock to Asian Economies in Bad and Good Times," IZA Discussion Papers 13274, Institute of Labor Economics (IZA).
    6. Victor Chernozhukov & Alfred Galichon & Marc Hallin & Marc Henry, 2015. "Monge-Kantorovich Depth, Quantiles, Ranks, and Signs," Working Papers hal-03460056, HAL.
    7. Eduardo Rubén Scarano, "undated". "La consolidación de la economía como disciplina científica en el país. El registro estadístico permanente," Documentos de trabajo del Instituto Interdisciplinario de Economía Política IIEP (UBA-CONICET) 202271, Universidad de Buenos Aires, Facultad de Ciencias Económicas, Instituto Interdisciplinario de Economía Política IIEP (UBA-CONICET).
    8. Keisuke Hirano & Jack R. Porter, 2023. "Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits," Papers 2302.03117, arXiv.org.
    9. Richard Blundell & Dennis Kristensen & Rosa Matzkin, 2017. "Individual counterfactuals with multidimensional unobserved heterogeneity," CeMMAP working papers 60/17, Institute for Fiscal Studies.
    10. Nadja Klein & Thomas Kneib, 2020. "Directional bivariate quantiles: a robust approach based on the cumulative distribution function," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 225-260, June.
    11. Marc Hallin, 2021. "Measure Transportation and Statistical Decision Theory," Working Papers ECARES 2021-04, ULB -- Universite Libre de Bruxelles.
    12. Florian Gunsilius & Susanne Schennach, 2023. "Independent Nonlinear Component Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(542), pages 1305-1318, April.
    13. Tongseok Lim, 2023. "Replication of financial derivatives under extreme market models given marginals," Papers 2307.00807, arXiv.org.
    14. Richard Spady & Sami Stouli, 2012. "Dual Regression," Papers 1210.6958, arXiv.org, revised Sep 2018.
    15. Roger Koenker, 2017. "Quantile regression 40 years on," CeMMAP working papers CWP36/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Nadja Klein & Torsten Hothorn & Luisa Barbanti & Thomas Kneib, 2022. "Multivariate conditional transformation models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 116-142, March.
    17. Walter W. Zhang & Sanjog Misra, 2022. "Coarse Personalization," Papers 2204.05793, arXiv.org, revised Aug 2024.
    18. Daouia, Abdelaati & Paindaveine, Davy, 2019. "Multivariate Expectiles, Expectile Depth and Multiple-Output Expectile Regression," TSE Working Papers 19-1022, Toulouse School of Economics (TSE), revised Feb 2023.
    19. Hongjian Shi & Mathias Drton & Marc Hallin & Fang Han, 2023. "Semiparametrically Efficient Tests of Multivariate Independence Using Center-Outward Quadrant, Spearman, and Kendall Statistics," Working Papers ECARES 2023-03, ULB -- Universite Libre de Bruxelles.
    20. Agarwal, Gaurav & Tu, Wei & Sun, Ying & Kong, Linglong, 2022. "Flexible quantile contour estimation for multivariate functional data: Beyond convexity," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    21. Marc Hallin & Gilles Mordant, 2021. "On the Finite-Sample Performance of Measure Transportation-Based Multivariate Rank Tests," Working Papers ECARES 2021-24, ULB -- Universite Libre de Bruxelles.
    22. Timo Dimitriadis & Yannick Hoga, 2022. "Dynamic CoVaR Modeling," Papers 2206.14275, arXiv.org, revised Feb 2024.
    23. Ruodu Wang & Zhenyuan Zhang, 2022. "Simultaneous Optimal Transport," Papers 2201.03483, arXiv.org, revised Dec 2024.
    24. Daniel Hlubinka & Lukáš Kotík & Miroslav Šiman, 2022. "Multivariate quantiles with both overall and directional probability interpretation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1586-1604, December.
    25. Eustasio del Barrio & Alberto González-Sanz & Marc Hallin, 2022. "Nonparametric Multiple-Output Center-Outward Quantile Regression," Working Papers ECARES 2022-10, ULB -- Universite Libre de Bruxelles.
    26. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Working Papers hal-03936221, HAL.
    27. Carlier, Guillaume & Chernozhukov, Victor & Galichon, Alfred, 2017. "Vector quantile regression beyond the specified case," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 96-102.
    28. Hubner, Stefan, 2023. "Identification of unobserved distribution factors and preferences in the collective household model," Journal of Econometrics, Elsevier, vol. 234(1), pages 301-326.

  91. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach," Papers 1501.03430, arXiv.org, revised Aug 2015.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Neng-Chieh Chang, 2020. "The Mode Treatment Effect," Papers 2007.11606, arXiv.org.
    3. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org, revised Nov 2024.
    4. Daniel Garcia & Juha Tolvanen & Alexander K. Wagner, 2022. "Demand Estimation Using Managerial Responses to Automated Price Recommendations," Management Science, INFORMS, vol. 68(11), pages 7918-7939, November.
    5. Christian Hansen & Damian Kozbur & Sanjog Misra, 2016. "Targeted undersmoothing," ECON - Working Papers 282, Department of Economics - University of Zurich, revised Apr 2018.
    6. Fan, Yanqin & Han, Fang & Li, Wei & Zhou, Xiao-Hua, 2020. "On rank estimators in increasing dimensions," Journal of Econometrics, Elsevier, vol. 214(2), pages 379-412.
    7. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    8. Jau-er Chen & Chien-Hsun Huang & Jia-Jyun Tien, 2021. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
    9. Brunow, Stephan & Lösch, Stefanie & Okhrin, Ostap, 2022. "Labor market tightness and individual wage growth: evidence from Germany," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 56, pages 1-16.
    10. Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2024. "Reprint: Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 239(2).
    11. Victor Chernozhukov & Whitney Newey & Vira Semenova, 2019. "Welfare Analysis in Dynamic Models," Papers 1908.09173, arXiv.org, revised Nov 2024.
    12. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    13. Markus Pelger & Jiacheng Zou, 2022. "Inference for Large Panel Data with Many Covariates," Papers 2301.00292, arXiv.org, revised Mar 2023.
    14. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio, 2017. "Economic Predictions with Big Data: The Illusion Of Sparsity," CEPR Discussion Papers 12256, C.E.P.R. Discussion Papers.
    15. Helmut Wasserbacher & Martin Spindler, 2021. "Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls," Papers 2107.04851, arXiv.org.
    16. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
    17. Kaila, Heidi & Azad, Abul, 2023. "The effects of crime and violence on food insecurity and consumption in Nigeria," Food Policy, Elsevier, vol. 115(C).
    18. Victor Chernozhukov & Whitney Newey & Rahul Singh, 2018. "De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers," Papers 1802.08667, arXiv.org, revised Oct 2022.
    19. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
    20. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    21. Stépahne Auray & Nicolas Lepage-Saucier & Purevdorj Tuvaandor, 2018. "Doubly Robust GMM Inference and Differentiated Products Demand Models," Working Papers 2018-13, Center for Research in Economics and Statistics.
    22. Jelena Bradic & Victor Chernozhukov & Whitney K. Newey & Yinchu Zhu, 2019. "Minimax Semiparametric Learning With Approximate Sparsity," Papers 1912.12213, arXiv.org, revised Aug 2022.
    23. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.
    24. Kueck, Jannis & Luo, Ye & Spindler, Martin & Wang, Zigan, 2023. "Estimation and inference of treatment effects with L2-boosting in high-dimensional settings," Journal of Econometrics, Elsevier, vol. 234(2), pages 714-731.
    25. Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2023. "Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 235(1), pages 166-179.
    26. Isaac K. Ofori & Camara K. Obeng & Simplice A. Asongu, 2024. "What Really Drives Economic Growth in Sub-Saharan Africa? Evidence from the Lasso Regularization and Inferential Techniques," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 144-179, March.
    27. Ofori, Isaac K. & Quaidoo, Christopher & Ofori, Pamela E., 2021. "What Drives Financial Sector Development in Africa? Insights from Machine Learning," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue forthcomi.
    28. Robert Adamek & Stephan Smeekes & Ines Wilms, 2020. "Lasso Inference for High-Dimensional Time Series," Papers 2007.10952, arXiv.org, revised Sep 2022.
    29. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
    30. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2017. "Non-separable Models with High-dimensional Data," Economics and Statistics Working Papers 15-2017, Singapore Management University, School of Economics.
    31. Rahul Singh, 2021. "Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimension," Papers 2102.11076, arXiv.org, revised Jul 2024.
    32. Jannis Kueck & Ye Luo & Martin Spindler & Zigan Wang, 2017. "Estimation and Inference of Treatment Effects with $L_2$-Boosting in High-Dimensional Settings," Papers 1801.00364, arXiv.org, revised Jul 2021.
    33. Tom Boot & Didier Nibbering, 2017. "Inference in high-dimensional linear regression models," Tinbergen Institute Discussion Papers 17-032/III, Tinbergen Institute, revised 05 Jul 2017.
    34. Yanqin Fan & Fang Han & Wei Li & Xiao-Hua Zhou, 2019. "On rank estimators in increasing dimensions," Papers 1908.05255, arXiv.org.
    35. Yoshimasa Uematsu & Takashi Yamagata, 2020. "Inference in Weak Factor Models," ISER Discussion Paper 1080, Institute of Social and Economic Research, Osaka University.
    36. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," THEMA Working Papers 2024-01, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    37. Yves Staudt & Joël Wagner, 2021. "Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance," Risks, MDPI, vol. 9(3), pages 1-28, March.
    38. Ali Charkhi & Gerda Claeskens, 2018. "Asymptotic post-selection inference for the Akaike information criterion," Biometrika, Biometrika Trust, vol. 105(3), pages 645-664.
    39. Doğan, Osman & Taşpınar, Süleyman & Bera, Anil K., 2021. "A Bayesian robust chi-squared test for testing simple hypotheses," Journal of Econometrics, Elsevier, vol. 222(2), pages 933-958.
    40. Geonwoo Kim & Suyong Song, 2024. "Double/Debiased CoCoLASSO of Treatment Effects with Mismeasured High-Dimensional Control Variables," Papers 2408.14671, arXiv.org.
    41. Victor Chernozhukov & Whitney K Newey & Rahul Singh, 2018. "Automatic Debiased Machine Learning of Causal and Structural Effects," Papers 1809.05224, arXiv.org, revised Oct 2022.
    42. Neng-Chieh Chang, 2018. "Semiparametric Difference-in-Differences with Potentially Many Control Variables," Papers 1812.10846, arXiv.org, revised Jan 2019.
    43. Victor Chernozhukov & Whitney K. Newey & James Robins, 2018. "Double/de-biased machine learning using regularized Riesz representers," CeMMAP working papers CWP15/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    44. Qizhao Chen & Vasilis Syrgkanis & Morgane Austern, 2022. "Debiased Machine Learning without Sample-Splitting for Stable Estimators," Papers 2206.01825, arXiv.org, revised Nov 2022.
    45. Barbara Felderer & Jannis Kueck & Martin Spindler, 2021. "Big Data meets Causal Survey Research: Understanding Nonresponse in the Recruitment of a Mixed-mode Online Panel," Papers 2102.08994, arXiv.org.
    46. Stephan Martin, 2022. "Estimation of Conditional Random Coefficient Models using Machine Learning Techniques," Papers 2201.08366, arXiv.org.
    47. Belloni, Alexandre & Hansen, Christian & Newey, Whitney, 2022. "High-dimensional linear models with many endogenous variables," Journal of Econometrics, Elsevier, vol. 228(1), pages 4-26.
    48. Ghysels, Eric & Qian, Hang, 2019. "Estimating MIDAS regressions via OLS with polynomial parameter profiling," Econometrics and Statistics, Elsevier, vol. 9(C), pages 1-16.
    49. Sandro Heiniger, 2024. "Data-driven model selection within the matrix completion method for causal panel data models," Papers 2402.01069, arXiv.org.

  92. Victor Chernozhukov & Wooyoung Kim & Sokbae (Simon) Lee & Adam Rosen, 2014. "Implementing intersection bounds in Stata," CeMMAP working papers 25/14, Institute for Fiscal Studies.

    Cited by:

    1. Hiroaki Kaido, 2014. "Asymptotically efficient estimation of weighted average derivatives with an interval censored variable," CeMMAP working papers 03/14, Institute for Fiscal Studies.
    2. Peter Eibich & Thomas Siedler, 2020. "Retirement, Intergenerational Time Transfers, and Fertility," SOEPpapers on Multidisciplinary Panel Data Research 1073, DIW Berlin, The German Socio-Economic Panel (SOEP).
    3. Ismael Mourifie & Marc Henry & Romuald Meango, 2017. "Sharp bounds and testability of a Roy model of STEM major choices," Papers 1709.09284, arXiv.org, revised Nov 2019.
    4. Kate Ho & Adam M. Rosen, 2015. "Partial Identification in Applied Research: Benefits and Challenges," NBER Working Papers 21641, National Bureau of Economic Research, Inc.
    5. Ismael Mourifie & Yuanyuan Wan, 2014. "Testing Local Average Treatment Effect Assumptions," Working Papers tecipa-514, University of Toronto, Department of Economics.
    6. Wooyoung Kim & Koohyun Kwon & Soonwoo Kwon & Sokbae Lee, 2018. "The identification power of smoothness assumptions in models with counterfactual outcomes," Quantitative Economics, Econometric Society, vol. 9(2), pages 617-642, July.
    7. Santiago Acerenza & Otávio Bartalotti & Désiré Kédagni, 2023. "Testing identifying assumptions in bivariate probit models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 407-422, April.
    8. Cl'ement de Chaisemartin & Diego Ciccia Xavier D'Haultf{oe}uille & Felix Knau, 2024. "Two-way Fixed Effects and Differences-in-Differences Estimators in Heterogeneous Adoption Designs," Papers 2405.04465, arXiv.org, revised Nov 2024.
    9. Giustinelli, Pamela & Manski, Charles F. & Molinari, Francesca, 2022. "Tail and center rounding of probabilistic expectations in the Health and Retirement Study," Journal of Econometrics, Elsevier, vol. 231(1), pages 265-281.
    10. Chalak, Karim & Kim, Daniel, 2020. "Measurement error in multiple equations: Tobin’s q and corporate investment, saving, and debt," Journal of Econometrics, Elsevier, vol. 214(2), pages 413-432.
    11. Andrew E Clark & Rong Zhu, 2023. "Taking Back Control? Quasi-Experimental Evidence on the Impact of Retirement on Locus of Control," PSE Working Papers halshs-04335808, HAL.
    12. Kyunghoon Ban & D'esir'e K'edagni, 2021. "Nonparametric Bounds on Treatment Effects with Imperfect Instruments," Papers 2109.14785, arXiv.org.
    13. Henry, Marc & Méango, Romuald & Mourifié, Ismaël, 2024. "Role models and revealed gender-specific costs of STEM in an extended Roy model of major choice," Journal of Econometrics, Elsevier, vol. 238(2).
    14. Dang Hai-Anh H. & Huang Yang & Selod Harris, 2020. "Children Left Behind in China: The Role of School Fees," IZA Journal of Development and Migration, Sciendo & Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 11(1), pages 1-29, January.
    15. Zahra Siddique, 2014. "Randomized control trials in an imperfect world," IZA World of Labor, Institute of Labor Economics (IZA), pages 110-110, December.
    16. Sun, Zhenting, 2023. "Instrument validity for heterogeneous causal effects," Journal of Econometrics, Elsevier, vol. 237(2).
    17. Francesca Molinari, 2020. "Microeconometrics with Partial Identi?cation," CeMMAP working papers CWP15/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. Bergeot, Julien & Tenand, Marianne, 2023. "Does informal care delay nursing home entry? Evidence from Dutch linked survey and administrative data," Journal of Health Economics, Elsevier, vol. 92(C).
    19. Kedagni, Desire, 2018. "Identifying Treatment Effects in the Presence of Confounded Types," ISU General Staff Papers 201809110700001056, Iowa State University, Department of Economics.
    20. Eibich, Peter, 2023. "Instrumental variable estimates of the burden of parental caregiving," The Journal of the Economics of Ageing, Elsevier, vol. 26(C).
    21. Jan Priebe, 2020. "Quasi-experimental evidence for the causal link between fertility and subjective well-being," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(3), pages 839-882, July.
    22. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    23. Romuald Méango, 2016. "What Makes Brain Drain More Likely? Evidence from Sub-Saharan Africa," CESifo Working Paper Series 6209, CESifo.
    24. Eibich, Peter & Lorenti, Angelo & Mosca, Irene, 2022. "Does retirement affect voluntary work provision? Evidence from Europe and the U.S," Labour Economics, Elsevier, vol. 76(C).
    25. Zhu, Rong & Onur, Ilke, 2023. "Does retirement (really) increase informal caregiving? Quasi-experimental evidence from Australia," Journal of Health Economics, Elsevier, vol. 87(C).

  93. Alexandre Belloni & Victor Chernozhukov & Christian Hansen & Damian Kozbur, 2014. "Inference in High Dimensional Panel Models with an Application to Gun Control," Papers 1411.6507, arXiv.org.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Achim Ahrens & Sean Lyons, 2021. "Do rising rents lead to longer commutes? A gravity model of commuting flows in Ireland," Urban Studies, Urban Studies Journal Limited, vol. 58(2), pages 264-279, February.
    3. Breinlich, Holger & Corradi, Valentina & Rocha, Nadia & Ruta, Michele & Santos Silva, JMC & Zylkin, Thomas, 2022. "Machine Learning in International Trade Research - Evaluating the Impact of Trade Agreements," CEPR Discussion Papers 17325, C.E.P.R. Discussion Papers.
    4. Julián Caballero & Christian Upper, 2023. "What happens to EMEs when US yields go up?," BIS Working Papers 1081, Bank for International Settlements.
    5. Moritz Meister & Annekatrin Niebuhr & Jan Cornelius Peters & Johannes Stiller, 2023. "Local attributes and migration balance – evidence for different age and skill groups from a machine learning approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(4), pages 794-825, May.
    6. Meera Mahadevan, 2024. "The Price of Power: Costs of Political Corruption in Indian Electricity," American Economic Review, American Economic Association, vol. 114(10), pages 3314-3344, October.
    7. Kaspar Wuthrich & Ying Zhu, 2019. "Omitted variable bias of Lasso-based inference methods: A finite sample analysis," Papers 1903.08704, arXiv.org, revised Sep 2021.
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    13. Anders Bredahl Kock & Haihan Tang, 2014. "Inference in High-dimensional Dynamic Panel Data Models," CREATES Research Papers 2014-58, Department of Economics and Business Economics, Aarhus University.
    14. Julián Caballero, 2020. "Corporate dollar debt and depreciations: all's well that ends well?," BIS Working Papers 879, Bank for International Settlements.
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    20. Damian Kozbur, 2015. "Testing-Based Forward Model Selection," ECON - Working Papers 283, Department of Economics - University of Zurich, revised Apr 2018.
    21. Marta Serra-Garcia & Uri Gneezy, 2023. "Improving Human Deception Detection Using Algorithmic Feedback," CESifo Working Paper Series 10518, CESifo.
    22. Vogt, M. & Walsh, C. & Linton, O., 2022. "CCE Estimation of High-Dimensional Panel Data Models with Interactive Fixed Effects," Cambridge Working Papers in Economics 2242, Faculty of Economics, University of Cambridge.
    23. Szabó-Morvai Ágnes & Hubert János Kiss, 2020. "Locus of control and Human Capital Investment Decisions: The Role of Effort, Parental Preferences and Financial Constraints," CERS-IE WORKING PAPERS 2055, Institute of Economics, Centre for Economic and Regional Studies.
    24. Aglasan, Serkan & Goodwin, Barry K. & Rejesus, Roderick, 2020. "Genetically Modified Rootworm-Resistant Corn, Risk, and Weather: Evidence from High Dimensional Methods," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 305181, Agricultural and Applied Economics Association.
    25. Achim Ahrens & Alessandra Stampi-Bombelli & Selina Kurer & Dominik Hangartner, 2023. "Optimal multi-action treatment allocation: A two-phase field experiment to boost immigrant naturalization," Papers 2305.00545, arXiv.org, revised Feb 2024.
    26. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," Papers 1501.03185, arXiv.org.
    27. Francesca Micocci & Armando Rungi, 2021. "Predicting Exporters with Machine Learning," Papers 2107.02512, arXiv.org, revised Sep 2022.
    28. Borgschulte, Mark & Vogler, Jacob, 2019. "Did the ACA Medicaid Expansion Save Lives?," IZA Discussion Papers 12552, Institute of Labor Economics (IZA).
    29. Qiu, Yun & Chen, Xi & Shi, Wei, 2020. "Impacts of Social and Economic Factors on the Transmission of Coronavirus Disease 2019 (COVID-19) in China," GLO Discussion Paper Series 494 [pre.], Global Labor Organization (GLO).
    30. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    31. Joseph S Shapiro, 2021. "The Environmental Bias of Trade Policy," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(2), pages 831-886.
    32. Michael Danquah & Abdul Malik Iddrisu & Ernest Owusu Boakye & Solomon Owusu, 2021. "Do gender wage differences within households influence women's empowerment and welfare?: Evidence from Ghana," WIDER Working Paper Series wp-2021-40, World Institute for Development Economic Research (UNU-WIDER).
    33. María Laura Alzua & Natalia Cantet & Ana C. Dammert & Damilola Olajide, 2023. "The Wellbeing Effects of an Old Age Pension: Experimental Evidence for Ekiti State in Nigeria," CEDLAS, Working Papers 0322, CEDLAS, Universidad Nacional de La Plata.
    34. Jonathan Fuhr & Dominik Papies, 2024. "Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions," Papers 2409.01266, arXiv.org.
    35. Mert Hakan Hekimoğlu & Burak Kazaz, 2020. "Analytics for Wine Futures: Realistic Prices," Production and Operations Management, Production and Operations Management Society, vol. 29(9), pages 2096-2120, September.
    36. Harold D. Chiang, 2019. "Many Average Partial Effects: with an Application to Text Regression," 2019 Papers pch1836, Job Market Papers.
    37. Achim Ahrens, 2015. "Civil conflicts in Africa: Climate, economic shocks, nighttime lights and spill-over effects," SEEC Discussion Papers 1501, Spatial Economics and Econometrics Centre, Heriot Watt University.
    38. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application," Papers 2008.03600, arXiv.org, revised Nov 2021.
    39. Damian Kozbur, 2020. "Analysis of Testing‐Based Forward Model Selection," Econometrica, Econometric Society, vol. 88(5), pages 2147-2173, September.
    40. Max Vilgalys, 2023. "A Machine Learning Approach to Measuring Climate Adaptation," Papers 2302.01236, arXiv.org.
    41. Falco J. Bargagli-Dtoffi & Massimo Riccaboni & Armando Rungi, 2020. "Machine Learning for Zombie Hunting. Firms Failures and Financial Constraints," Working Papers 01/2020, IMT School for Advanced Studies Lucca, revised Jun 2020.
    42. Carlos Lamarche & Thomas Parker, 2022. "Wild Bootstrap Inference For Penalized Quantile Regression For Longitudinal Data," Working Papers 22003 Classification-C15,, University of Waterloo, Department of Economics.
    43. Samuel Dodini, 2023. "Insurance Subsidies, the Affordable Care Act, and Financial Stability," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 42(1), pages 97-136, January.
    44. Collins, Alan & Fan, Jingwen & Mahabir, Aruneema, 2022. "Actual versus ‘natural’ rates of suicide: Evidence from the USA," Economic Modelling, Elsevier, vol. 106(C).
    45. Wei Shi & Lung-fei Lee, 2018. "The effects of gun control on crimes: a spatial interactive fixed effects approach," Empirical Economics, Springer, vol. 55(1), pages 233-263, August.
    46. Harold D. Chiang & Kengo Kato & Yukun Ma & Yuya Sasaki, 2019. "Multiway Cluster Robust Double/Debiased Machine Learning," Papers 1909.03489, arXiv.org, revised Mar 2020.
    47. Duncan Sheppard Gilchrist & Emily Glassberg Sands, 2016. "Something to Talk About: Social Spillovers in Movie Consumption," Journal of Political Economy, University of Chicago Press, vol. 124(5), pages 1339-1382.
    48. Falco J. Bargagli-Stoffi & Fabio Incerti & Massimo Riccaboni & Armando Rungi, 2023. "Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values," Papers 2306.08165, arXiv.org.
    49. Vogt, M. & Walsh, C. & Linton, O., 2022. "CCE Estimation of High-Dimensional Panel Data Models with Interactive Fixed Effects," Janeway Institute Working Papers 2218, Faculty of Economics, University of Cambridge.
    50. James M. Carson & Cameron M. Ellis & Robert E. Hoyt & Krzysztof Ostaszewski, 2020. "Sunk Costs and Screening: Two‐Part Tariffs in Life Insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 87(3), pages 689-718, September.
    51. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," THEMA Working Papers 2024-01, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    52. Sander Gerritsen & Mark Kattenberg & Sonny Kuijpers, 2019. "The impact of age at arrival on education and mental health," CPB Discussion Paper 389, CPB Netherlands Bureau for Economic Policy Analysis.
    53. J. Daniel Aromí & M. Paula Bonel & Julián Cristiá & Martín Llada, 2020. "Socio-economic status and mobility during the COVID-19 pandemic: An analysis of large Latin American urban areas," Asociación Argentina de Economía Política: Working Papers 4307, Asociación Argentina de Economía Política.
    54. Carlos Aller & Lorenzo Ductor & Daryna Grechyna, 2020. "Robust Determinants of CO2 Emissions," ThE Papers 20/13, Department of Economic Theory and Economic History of the University of Granada..
    55. Damian Kozbur, 2013. "Inference in additively separable models with a high-dimensional set of conditioning variables," ECON - Working Papers 284, Department of Economics - University of Zurich, revised Apr 2018.
    56. Xi Chen & Ye Luo & Martin Spindler, 2019. "Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data," Papers 1912.12867, arXiv.org, revised Jan 2020.
    57. Oliver Linton & Maximilian Ruecker & Michael Vogt & Christopher Walsh, 2022. "Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects," Papers 2206.12152, arXiv.org, revised Nov 2024.
    58. Fonseca Morello, Thiago, 2023. "Hospitalization due to fire-induced pollution in the Brazilian Amazon: A causal inference analysis with an assessment of policy trade-offs," World Development, Elsevier, vol. 161(C).
    59. Luv Sharma & Aravind Chandrasekaran & Elliot Bendoly, 2020. "Does the Office of Patient Experience Matter in Improving Delivery of Care?," Production and Operations Management, Production and Operations Management Society, vol. 29(4), pages 833-855, April.
    60. Chiang, Harold D. & Rodrigue, Joel & Sasaki, Yuya, 2023. "Post-Selection Inference In Three-Dimensional Panel Data," Econometric Theory, Cambridge University Press, vol. 39(3), pages 623-658, June.
    61. Brian Asquith, 2019. "Do Rent Increases Reduce the Housing Supply Under Rent Control? Evidence from Evictions in San Francisco," Upjohn Working Papers 19-296, W.E. Upjohn Institute for Employment Research.
    62. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
    63. Damian Kozbur, 2017. "Sharp convergence rates for forward regression in high-dimensional sparse linear models," ECON - Working Papers 253, Department of Economics - University of Zurich, revised Apr 2018.
    64. Natalie Bau & Martin Rotemberg & Manisha Shah & Bryce Steinberg, 2020. "Human Capital Investment in the Presence of Child Labor," NBER Working Papers 27241, National Bureau of Economic Research, Inc.
    65. Davide Viviano & Jelena Bradic, 2021. "Dynamic covariate balancing: estimating treatment effects over time with potential local projections," Papers 2103.01280, arXiv.org, revised Jan 2024.
    66. Rossmann, Tobias, 2019. "Does Experience Shape Subjective Expectations?," Rationality and Competition Discussion Paper Series 181, CRC TRR 190 Rationality and Competition.

  94. Guillaume Carlier & Victor Chernozhukov & Alfred Galichon, 2014. "Vector quantile regression," CeMMAP working papers 48/14, Institute for Fiscal Studies.

    Cited by:

    1. Victor Chernozhukov & Alfred Galichon & Marc Hallin & Marc Henry, 2015. "Monge-Kantorovich Depth, Quantiles, Ranks, and Signs," Working Papers hal-03460056, HAL.
    2. María Edo & Walter Sosa Escudero & Marcela Svarc, 2021. "A multidimensional approach to measuring the middle class," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(1), pages 139-162, March.
    3. Stefan Holst Bache & Christian M. Dahl & Johannes Tang, "undated". "Headlights on tobacco road to low birthweight outcomes - Evidence from a battery of quantile regression estimators and a heterogeneous panelCreation-Date: 20080508," CREATES Research Papers 2008-20, Department of Economics and Business Economics, Aarhus University.

  95. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2014. "Program evaluation with high-dimensional data," CeMMAP working papers 33/14, Institute for Fiscal Studies.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
    2. Christian Hansen & Damian Kozbur & Sanjog Misra, 2016. "Targeted undersmoothing," ECON - Working Papers 282, Department of Economics - University of Zurich, revised Apr 2018.
    3. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers 62/13, Institute for Fiscal Studies.
    4. Christian Hansen & Yuan Liao, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," Papers 1611.09420, arXiv.org, revised Dec 2016.
    5. Alexandre Belloni & Victor Chernozhukov & Christian Hansen & Damian Kozbur, 2014. "Inference in High Dimensional Panel Models with an Application to Gun Control," Papers 1411.6507, arXiv.org.
    6. Damian Kozbur, 2015. "Testing-Based Forward Model Selection," ECON - Working Papers 283, Department of Economics - University of Zurich, revised Apr 2018.
    7. Matias D. Cattaneo & Michael Jansson, 2014. "Bootstrapping Kernel-Based Semiparametric Estimators," CREATES Research Papers 2014-25, Department of Economics and Business Economics, Aarhus University.
    8. Michael Danquah & Abdul Malik Iddrisu & Ernest Owusu Boakye & Solomon Owusu, 2021. "Do gender wage differences within households influence women's empowerment and welfare?: Evidence from Ghana," WIDER Working Paper Series wp-2021-40, World Institute for Development Economic Research (UNU-WIDER).
    9. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
    10. Xu, Ning & Hong, Jian & Fisher, Timothy, 2016. "Finite-sample and asymptotic analysis of generalization ability with an application to penalized regression," MPRA Paper 73622, University Library of Munich, Germany.
    11. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    12. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers 47/16, Institute for Fiscal Studies.
    13. Max H. Farrell, 2013. "Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations," Papers 1309.4686, arXiv.org, revised Feb 2018.
    14. Kasy Maximilian, 2019. "Uniformity and the Delta Method," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-19, January.
    15. Shao, Shuai & Xu, Le & Yang, Lili & Yu, Dianfan, 2024. "How do energy-saving policies improve environmental quality: Evidence from China’s Top 10,000 energy-consuming enterprises program," World Development, Elsevier, vol. 175(C).
    16. Guo, Zijian & Kang, Hyunseung & Cai, T. Tony & Small, Dylan S., 2018. "Testing endogeneity with high dimensional covariates," Journal of Econometrics, Elsevier, vol. 207(1), pages 175-187.
    17. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers CWP47/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. Braverman, Mark & Chassang, Sylvain, 2022. "Data-driven incentive alignment in capitation schemes," Journal of Public Economics, Elsevier, vol. 207(C).
    19. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
    20. Victor Chernozhukov & Vira Semenova, 2018. "Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions," CeMMAP working papers CWP40/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    21. Kaspar W thrich, 2015. "Semiparametric estimation of quantile treatment effects with endogeneity," Diskussionsschriften dp1509, Universitaet Bern, Departement Volkswirtschaft.
    22. Susan Athey & Guido Imbens, 2016. "The State of Applied Econometrics - Causality and Policy Evaluation," Papers 1607.00699, arXiv.org.
    23. Thai T. Pham & Yuanyuan Shen, 2017. "A Deep Causal Inference Approach to Measuring the Effects of Forming Group Loans in Online Non-profit Microfinance Platform," Papers 1706.02795, arXiv.org.

  96. Victor Chernozhukov & Alfred Galichon & Marc Hallin & Marc Henry, 2014. "Monge-Kantorovich Depth, Quantiles, Ranks, and Signs," Papers 1412.8434, arXiv.org, revised Sep 2015.

    Cited by:

    1. Marc Hallin & Daniel Hlubinka & Šárka Hudecová, 2023. "Efficient Fully Distribution-Free Center-Outward Rank Tests for Multiple-Output Regression and MANOVA," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(543), pages 1923-1939, July.
    2. Marcel Klatt & Axel Munk & Yoav Zemel, 2022. "Limit laws for empirical optimal solutions in random linear programs," Annals of Operations Research, Springer, vol. 315(1), pages 251-278, August.
    3. Dmitry Arkhangelsky, 2019. "Dealing with a Technological Bias: The Difference-in-Difference Approach," Working Papers wp2019_1903, CEMFI.
    4. Gunsilius, Florian F., 2023. "A condition for the identification of multivariate models with binary instruments," Journal of Econometrics, Elsevier, vol. 235(1), pages 220-238.
    5. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," SciencePo Working papers Main hal-03936221, HAL.
    6. Hamel, Andreas H. & Kostner, Daniel, 2018. "Cone distribution functions and quantiles for multivariate random variables," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 97-113.
    7. Marc Hallin & Hongjian Shi & Mathias Drton & Fang Han, 2021. "Center-Outward Sign- and Rank-Based Quadrant, Spearman, and Kendall Tests for Multivariate Independence," Working Papers ECARES 2021-27, ULB -- Universite Libre de Bruxelles.
    8. Manuel Arellano & Stéphane Bonhomme, 2019. "Recovering Latent Variables by Matching," Working Papers wp2019_1914, CEMFI.
    9. María Edo & Walter Sosa Escudero & Marcela Svarc, 2021. "A multidimensional approach to measuring the middle class," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(1), pages 139-162, March.
    10. Lixiong Li & Marc Henry, 2022. "Finite Sample Inference in Incomplete Models," Papers 2204.00473, arXiv.org, revised Apr 2024.
    11. Marc Hallin, 2021. "Measure Transportation and Statistical Decision Theory," Working Papers ECARES 2021-04, ULB -- Universite Libre de Bruxelles.
    12. Hudecová, Šárka & Šiman, Miroslav, 2022. "Multivariate ranks based on randomized lift-interdirections," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    13. Yanqin Fan & Marc Henry, 2020. "Vector copulas," Papers 2009.06558, arXiv.org, revised Apr 2021.
    14. Marc Hallin & Davide La Vecchia & Hang Liu, 2020. "Rank-Based Testing for Semiparametric VAR Models: a measure transportation approach," Working Papers ECARES 2020-47, ULB -- Universite Libre de Bruxelles.
    15. Florian Gunsilius & Susanne Schennach, 2023. "Independent Nonlinear Component Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(542), pages 1305-1318, April.
    16. M. Hallin & D. La Vecchia & H. Liu, 2022. "Center-Outward R-Estimation for Semiparametric VARMA Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(538), pages 925-938, April.
    17. Kotík, Lukáš & Hlubinka, Daniel, 2017. "A weighted localization of halfspace depth and its properties," Journal of Multivariate Analysis, Elsevier, vol. 157(C), pages 53-69.
    18. Alberto González-Sanz & Marc Hallin & Bodhisattva Sen, 2023. "Monotone Measure-Preserving Maps in Hilbert Spaces: Existence, Uniqueness, and Stability," Working Papers ECARES 2023-10, ULB -- Universite Libre de Bruxelles.
    19. Olivier Paul Faugeras & Ludger Rüschendorf, 2021. "Functional, randomized and smoothed multivariate quantile regions," Post-Print hal-03352330, HAL.
    20. Marc Hallin, 2018. "From Mahalanobis to Bregman via Monge and Kantorovich towards a “General Generalised Distance”," Working Papers ECARES 2018-12, ULB -- Universite Libre de Bruxelles.
    21. Fan, Yanqin & Henry, Marc, 2023. "Vector copulas," Journal of Econometrics, Elsevier, vol. 234(1), pages 128-150.
    22. Marc Hallin & Daniel Hlubinka & Sarka Hudecova, 2020. "Fully Distribution-free Center-outward Rank Tests for Multiple-output Regression and Manova," Working Papers ECARES 2020-32, ULB -- Universite Libre de Bruxelles.
    23. Jan Bierlant & Sven Buitendag & Eustasio Del Barrio & Marc Hallin, 2019. "Center-Outward Quantiles And The Measurement Of Multivariate Risk," Working Papers ECARES 2019-30, ULB -- Universite Libre de Bruxelles.
    24. Petra Laketa & Stanislav Nagy, 2022. "Halfspace depth for general measures: the ray basis theorem and its consequences," Statistical Papers, Springer, vol. 63(3), pages 849-883, June.
    25. Davy Paindaveine & Germain Van Bever, 2017. "Halfspace Depths for Scatter, Concentration and Shape Matrices," Working Papers ECARES ECARES 2017-19, ULB -- Universite Libre de Bruxelles.
    26. Marc Hallin & Hang Liu, 2022. "Center-outward Rank- and Sign-based VARMA Portmanteau Tests," Working Papers ECARES 2022-27, ULB -- Universite Libre de Bruxelles.
    27. Florian Gunsilius, 2018. "Point-identification in multivariate nonseparable triangular models," Papers 1806.09680, arXiv.org.
    28. Marc Hallin, 2018. "From Mahalanobis to Bregman via Monge and Kantorovich," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 135-146, December.
    29. Yanqin Fan & Marc Henry & Brendan Pass & Jorge A. Rivero, 2022. "Lorenz map, inequality ordering and curves based on multidimensional rearrangements," Papers 2203.09000, arXiv.org, revised Apr 2024.
    30. Faugeras, Olivier P. & Rüschendorf, Ludger, 2021. "Functional, randomized and smoothed multivariate quantile regions," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    31. Hongjian Shi & Mathias Drton & Marc Hallin & Fang Han, 2023. "Semiparametrically Efficient Tests of Multivariate Independence Using Center-Outward Quadrant, Spearman, and Kendall Statistics," Working Papers ECARES 2023-03, ULB -- Universite Libre de Bruxelles.
    32. Marc Hallin & H Lui & Thomas Verdebout, 2022. "Nonparametric Measure-transportation-based Methods for Directional Data," Working Papers ECARES 2022-18, ULB -- Universite Libre de Bruxelles.
    33. Marc Hallin & Gilles Mordant, 2021. "On the Finite-Sample Performance of Measure Transportation-Based Multivariate Rank Tests," Working Papers ECARES 2021-24, ULB -- Universite Libre de Bruxelles.
    34. Hongjian Shi & Marc Hallin & Mathias Drton & Fang Han, 2020. "Rate-Optimality of Consistent Distribution-Free Tests of Independence Based on Center-Outward Ranks and Signs," Working Papers ECARES 2020-23, ULB -- Universite Libre de Bruxelles.
    35. Eustasio Del Barrio & Juan Cuesta Albertos & Marc Hallin & Carlos Matran, 2018. "Smooth Cyclically Monotone Interpolation and Empirical Center-Outward Distribution Functions," Working Papers ECARES 2018-15, ULB -- Universite Libre de Bruxelles.
    36. Eustasio del Barrio & Alberto González-Sanz & Marc Hallin, 2022. "Nonparametric Multiple-Output Center-Outward Quantile Regression," Working Papers ECARES 2022-10, ULB -- Universite Libre de Bruxelles.
    37. Alfred Galichon & Bernard Salani'e, 2021. "Cupid's Invisible Hand: Social Surplus and Identification in Matching Models," Papers 2106.02371, arXiv.org, revised Jan 2023.
    38. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Working Papers hal-03936221, HAL.

  97. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers 59/13, Institute for Fiscal Studies.

    Cited by:

    1. Andrii Babii & Xi Chen & Eric Ghysels & Rohit Kumar, 2020. "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice," Papers 2010.08463, arXiv.org, revised Nov 2021.
    2. Gonzalez, Felipe & Prem, Mounu & von Dessauer, Cristine, 2023. "Empowerment or Indoctrination? Women Centers Under Dictatorship," SocArXiv 64mf9, Center for Open Science.
    3. Emily Cuddy & Janet Currie, 2020. "Rules vs. Discretion: Treatment of Mental Illness in U.S. Adolescents," Working Papers 2020-10, Princeton University. Economics Department..
    4. TELLO, Mario D., 2024. "Inversión Pública En Infraestructura Y Crecimiento Regional En Perú, 2005-2020: Un Análisis Basado En Técnicas De Aprendizaje Automático Causal," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 24(2), pages 195-222.
    5. Abhijit Banerjee & Esther Duflo & Amy Finkelstein & Lawrence F. Katz & Benjamin A. Olken & Anja Sautmann, 2020. "In Praise of Moderation: Suggestions for the Scope and Use of Pre-Analysis Plans for RCTs in Economics," NBER Working Papers 26993, National Bureau of Economic Research, Inc.
    6. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023. "Granger Causality Testing in High-Dimensional VARs: A Post-Double-Selection Procedure," Journal of Financial Econometrics, Oxford University Press, vol. 21(3), pages 915-958.
    7. Dehejia, Rajeev & Pop-Eleches, Cristian & Samii, Cyrus, 2015. "From Local to Global: External Validity in a Fertility Natural Experiment," IZA Discussion Papers 9300, Institute of Labor Economics (IZA).
    8. Michael Bailey & Drew Johnston & Theresa Kuchler & Johannes Stroebel & Arlene Wong, 2019. "Peer effects in product adoption," CESifo Working Paper Series 7685, CESifo.
    9. Mounu Prem & Juan F. Vargas & Olga Namen, 2023. "The Human Capital Peace Dividend," Journal of Human Resources, University of Wisconsin Press, vol. 58(3), pages 962-1002.
    10. Mejía, D & Prem, M & Vargas, J. F, 2019. "The Rise and Persistence of Illegal Crops: Evidence from a Naive Policy Announcement," Documentos de Trabajo 17552, Universidad del Rosario.
    11. Gerald A. Carlino & Albert Salz, 2019. "Beautiful City: Leisure Amenities and Urban Growth," Working Papers 19-16, Federal Reserve Bank of Philadelphia.
    12. Anil Kumar, 2014. "Do restrictions on home equity extraction contribute to lower mortgage defaults? evidence from a policy discontinuity at the Texas border," Working Papers 1410, Federal Reserve Bank of Dallas.
    13. Susan Athey & Guido Imbens, 2019. "Machine Learning Methods Economists Should Know About," Papers 1903.10075, arXiv.org.
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    166. Marco Taboga, 2019. "Cross-country differences in the size of venture capital financing rounds: a machine learning approach," Temi di discussione (Economic working papers) 1243, Bank of Italy, Economic Research and International Relations Area.
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    171. Pushan Dutt & Ilia Tsetlin, 2021. "Income distribution and economic development: Insights from machine learning," Economics and Politics, Wiley Blackwell, vol. 33(1), pages 1-36, March.
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    178. Brett R. Gordon & Mitchell J. Lovett & Bowen Luo & James C. Reeder, 2023. "Disentangling the Effects of Ad Tone on Voter Turnout and Candidate Choice in Presidential Elections," Management Science, INFORMS, vol. 69(1), pages 220-243, January.
    179. Ng Cheuk Fai, 2022. "Robust Inference in High Dimensional Linear Model with Cluster Dependence," Papers 2212.05554, arXiv.org.
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    181. Cerulli, Giovanni, 2020. "A Super-Learning Machine for Predicting Economic Outcomes," MPRA Paper 99111, University Library of Munich, Germany.
    182. Dominick Bartelme & Andrei Levchenko & Ting Lan, 2019. "Specialization, Market Access and Medium-Term Growth," 2019 Meeting Papers 999, Society for Economic Dynamics.
    183. Marcel Fafchamps & Julien Labonne, 2016. "Using Split Samples to Improve Inference about Causal Effects," NBER Working Papers 21842, National Bureau of Economic Research, Inc.
    184. d'Adda, Giovanna & Mahmud, Mahreen & Said, Farah & Bonan, Jacopo, 2020. "The Role of Flexibility and Planning in Repayment Discipline: Evidence from a Field Experiment on Pay-as-You-Go Off-Grid Electricity," RFF Working Paper Series 20-14, Resources for the Future.
    185. Anastasiya-Mariya Asanov Noha & Igor Asanov & Guido Buenstorf, 2022. "Mental Health and Stress Level of Ukrainians Seeking Psychological Help Online," MAGKS Papers on Economics 202231, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    186. Geonwoo Kim & Suyong Song, 2024. "Double/Debiased CoCoLASSO of Treatment Effects with Mismeasured High-Dimensional Control Variables," Papers 2408.14671, arXiv.org.
    187. Skoufias,Emmanuel & Vinha,Katja Pauliina, 2020. "Child Stature, Maternal Education, and Early Childhood Development," Policy Research Working Paper Series 9396, The World Bank.
    188. Arthur Acolin & Ari Decter-Frain & Matt Hall, 2022. "Small-area estimates from consumer trace data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(27), pages 843-882.
    189. Leander Andres & Marc Fabel & Helmut Rainer, 2021. "How Much Violence Does Football Hooliganism Cause?," CESifo Working Paper Series 9431, CESifo.
    190. Dimic, Nebojsa & Goodell, John W. & Piljak, Vanja & Vulanovic, Milos, 2023. "Acquisition determinants of energy SPACs: Reflecting a closed group?," Finance Research Letters, Elsevier, vol. 55(PB).
    191. Merino Troncoso, Carlos, 2023. "Introduction to Competition Economics," MPRA Paper 115999, University Library of Munich, Germany.
    192. Makarin, Alexey & Guiso, Luigi, 2020. "Affinity, Trust, and Information," CEPR Discussion Papers 15250, C.E.P.R. Discussion Papers.
    193. Fonseca Morello, Thiago, 2023. "Hospitalization due to fire-induced pollution in the Brazilian Amazon: A causal inference analysis with an assessment of policy trade-offs," World Development, Elsevier, vol. 161(C).
    194. Brito, Igor R.S. & Oliveira, Alessandro V.M. & Dresner, Martin E., 2021. "An econometric study of the effects of airport privatization on airfares in Brazil," Transport Policy, Elsevier, vol. 114(C), pages 338-349.
    195. Gray, Colin, 2019. "Leaving benefits on the table: Evidence from SNAP," Journal of Public Economics, Elsevier, vol. 179(C).
    196. Akash Malhotra, 2021. "A hybrid econometric–machine learning approach for relative importance analysis: prioritizing food policy," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 549-581, September.
    197. Auricchio, Marta & Ciani, Emanuele & Dalmazzo, Alberto & de Blasio, Guido, 2020. "Redistributive public employment? A test for the South of Italy," Economics Letters, Elsevier, vol. 186(C).
    198. Nicolás de Roux & Luis Roberto Martínez, 2021. "Forgone Investment: Civil Conflict and Agricultural Credit in Colombia," Documentos CEDE 19236, Universidad de los Andes, Facultad de Economía, CEDE.
    199. Jermain C. Kaminski & Christian Hopp, 2020. "Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals," Small Business Economics, Springer, vol. 55(3), pages 627-649, October.
    200. Chaisemartin, Clement de & Navarrete, Nicolas, 2019. "The direct and spillover effects of a mental health program for disruptive students," CAGE Online Working Paper Series 401, Competitive Advantage in the Global Economy (CAGE).
    201. Yusuke Narita, 2020. "A Theory of Quasi-Experimental Evaluation of School Quality," Working Papers 2020-085, Human Capital and Economic Opportunity Working Group.
    202. Andreas Fuster & Paul Goldsmith‐Pinkham & Tarun Ramadorai & Ansgar Walther, 2022. "Predictably Unequal? The Effects of Machine Learning on Credit Markets," Journal of Finance, American Finance Association, vol. 77(1), pages 5-47, February.
    203. Diego A. Martin, 2024. "Women Seeking Jobs with Limited Information: Evidence from Iraq," CID Working Papers 157a, Center for International Development at Harvard University.
    204. Clarissa Lotti & Giancarlo Spagnolo, 2022. "Indirect Savings from Public Procurement Centralization," CEIS Research Paper 532, Tor Vergata University, CEIS, revised 01 Feb 2022.
    205. Eufrásio, Ana Beatriz R. & Eller, Rogéria A.G. & Oliveira, Alessandro V.M., 2021. "Are on-time performance statistics worthless? An empirical study of the flight scheduling strategies of Brazilian airlines," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    206. Karolis Matikonis & Matthew Gobey, 2024. "Small Business Property Tax Reductions and Firm Productivity," Small Business Economics, Springer, vol. 62(1), pages 307-324, January.
    207. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2018. "Prediction, Judgment, and Complexity: A Theory of Decision-Making and Artificial Intelligence," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 89-110, National Bureau of Economic Research, Inc.
    208. Saavedra, Santiago & Romero, Mauricio, 2021. "Local incentives and national tax evasion: The response of illegal mining to a tax reform in Colombia," European Economic Review, Elsevier, vol. 138(C).
    209. Faber, Marius, 2020. "Robots and reshoring: Evidence from Mexican labor markets," Journal of International Economics, Elsevier, vol. 127(C).
    210. D’Amour, Alexander & Ding, Peng & Feller, Avi & Lei, Lihua & Sekhon, Jasjeet, 2021. "Overlap in observational studies with high-dimensional covariates," Journal of Econometrics, Elsevier, vol. 221(2), pages 644-654.
    211. Susanna Loeb & Michala Iben Riis-Vestergaard & Marianne Simonsen, 2023. "Supporting Language Development through a Texting Program: Initial Results from Denmark," Economics Working Papers 2023-01, Department of Economics and Business Economics, Aarhus University.
    212. Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2021. "A Machine Learning Approach to Analyze and Support Anti-Corruption Policy," CESifo Working Paper Series 9015, CESifo.
    213. Belloni, Alexandre & Hansen, Christian & Newey, Whitney, 2022. "High-dimensional linear models with many endogenous variables," Journal of Econometrics, Elsevier, vol. 228(1), pages 4-26.
    214. Hector F. Calvo-Pardo & Tullio Mancini & Jose Olmo, 2020. "Neural Network Models for Empirical Finance," JRFM, MDPI, vol. 13(11), pages 1-22, October.
    215. Erica M. Field & Rohini Pande & Natalia Rigol & Simone G. Schaner & Charity Troyer Moore, 2019. "On Her Own Account: How Strengthening Women's Financial Control Affects Labor Supply and Gender Norms," NBER Working Papers 26294, National Bureau of Economic Research, Inc.
    216. Andini, Monica & Boldrini, Michela & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Paladini, Andrea, 2022. "Machine learning in the service of policy targeting: The case of public credit guarantees," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 434-475.
    217. Rishabh Tyagi & Peter Eibich & Vegard Skirbekk, 2024. "Gender norms and partnership dissolution following involuntary job loss in Germany," MPIDR Working Papers WP-2024-027, Max Planck Institute for Demographic Research, Rostock, Germany.
    218. Huntington-Klein Nick, 2020. "Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 182-208, January.
    219. Fabio Pammolli & Paolo Bonaretti & Massimo Riccaboni & Valentina Tortolini, 2019. "Quali Regole per la Spesa Farmaceutica? - Criticità, Impatti, Proposte," Working Papers CERM 01-2019, Competitività, Regole, Mercati (CERM).
    220. Patricia Boyallian & Pablo Ruiz-Verdú, 2018. "Leverage, CEO Risk-Taking Incentives, and Bank Failure during the 2007–10 Financial Crisis [Endogenous matching and the empirical determinants of contract form]," Review of Finance, European Finance Association, vol. 22(5), pages 1763-1805.
    221. Li, Zhaoyuan & Yao, Jianfeng, 2019. "Testing for heteroscedasticity in high-dimensional regressions," Econometrics and Statistics, Elsevier, vol. 9(C), pages 122-139.
    222. Bai, Jushan & Liao, Yuan, 2017. "Inferences in panel data with interactive effects using large covariance matrices," Journal of Econometrics, Elsevier, vol. 200(1), pages 59-78.
    223. Sokbae Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2022. "Fast Inference for Quantile Regression with Tens of Millions of Observations," Papers 2209.14502, arXiv.org, revised Oct 2023.
    224. Hui-Ching Chuang & Jau-er Chen, 2023. "Exploring Industry-Distress Effects on Loan Recovery: A Double Machine Learning Approach for Quantiles," Econometrics, MDPI, vol. 11(1), pages 1-20, February.
    225. Oliveira, Alessandro V.M. & Oliveira, Bruno F. & Vassallo, Moisés D., 2023. "Airport service quality perception and flight delays: Examining the influence of psychosituational latent traits of respondents in passenger satisfaction surveys," Research in Transportation Economics, Elsevier, vol. 102(C).
    226. Alex Krumer & Michael Lechner, 2018. "Midweek Effect On Soccer Performance: Evidence From The German Bundesliga," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 193-207, January.
    227. Alison Andrew & Sonya Krutikova & Gabriela Smarrelli & Hemlata Verma, 2022. "Gender norms, violence and adolescent girls’ trajectories: evidence from a field experiment in India," IFS Working Papers W22/41, Institute for Fiscal Studies.
    228. Michael Danquah & Solomon Owusu, 2021. "Digital technology and productivity of informal enterprises: Empirical evidence from Nigeria," WIDER Working Paper Series wp-2021-114, World Institute for Development Economic Research (UNU-WIDER).
    229. Shengying Zhai & Qihui Chen & Wenxin Wang, 2019. "What Drives Green Fodder Supply in China?—A Nerlovian Analysis with LASSO Variable Selection," Sustainability, MDPI, vol. 11(23), pages 1-17, November.
    230. Marta Auricchio & Emanuele Ciani & Alberto Dalmazzo & Guido de Blasio, 2017. "The consequences of public employment: evidence from Italian municipalities," Temi di discussione (Economic working papers) 1125, Bank of Italy, Economic Research and International Relations Area.
    231. Danilo Cavapozzi, 2022. "The causal impact of remote working on depression during the first wave of the COVID-19 pandemic," French Stata Users' Group Meetings 2022 10, Stata Users Group.
    232. David C. Phillips, 2017. "Stopping on Nine: Evidence of Heuristic Managerial Decision‐Making in Major League Baseball Pitcher Substitutions," Southern Economic Journal, John Wiley & Sons, vol. 84(2), pages 577-599, October.
    233. Tianxi Cai & T. Tony Cai & Zijian Guo, 2021. "Optimal statistical inference for individualized treatment effects in high‐dimensional models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 669-719, September.

  98. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "On the asymptotic theory for least squares series: pointwise and uniform results," CeMMAP working papers 73/13, Institute for Fiscal Studies.

    Cited by:

    1. Victor Chernozhukov & Ivan Fernandez-Val & Stefan Hoderlein & Hajo Holzmann & Whitney K. Newey, 2013. "Nonparametric identification in panels using quantiles," CeMMAP working papers 66/13, Institute for Fiscal Studies.
    2. Ying-Ying Lee, 2014. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Economics Series Working Papers 706, University of Oxford, Department of Economics.

  99. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Uniform Post Selection Inference for LAD Regression and Other Z-estimation problems," Papers 1304.0282, arXiv.org, revised Oct 2020.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
    2. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2020. "Unconditional Quantile Regression with High Dimensional Data," Papers 2007.13659, arXiv.org, revised Feb 2022.
    4. Fan, Yanqin & Han, Fang & Li, Wei & Zhou, Xiao-Hua, 2020. "On rank estimators in increasing dimensions," Journal of Econometrics, Elsevier, vol. 214(2), pages 379-412.
    5. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers 62/13, Institute for Fiscal Studies.
    6. Christian Hansen & Yuan Liao, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," Papers 1611.09420, arXiv.org, revised Dec 2016.
    7. Sven Klaassen & Jannis Kuck & Martin Spindler & Victor Chernozhukov, 2018. "Uniform Inference in High-Dimensional Gaussian Graphical Models," Papers 1808.10532, arXiv.org, revised Dec 2018.
    8. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2016. "Post-Selection Inference for Generalized Linear Models With Many Controls," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 606-619, October.
    9. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," Papers 1501.03185, arXiv.org.
    10. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2016. "Valid post-selection and post-regularization inference: An elementary, general approach," CeMMAP working papers CWP36/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
    12. Sven Klaassen & Jannis Kueck & Martin Spindler, 2017. "Transformation Models in High-Dimensions," Papers 1712.07364, arXiv.org.
    13. Jelena Bradic & Victor Chernozhukov & Whitney K. Newey & Yinchu Zhu, 2019. "Minimax Semiparametric Learning With Approximate Sparsity," Papers 1912.12213, arXiv.org, revised Aug 2022.
    14. Chen, Le-Yu & Lee, Sokbae, 2023. "Sparse quantile regression," Journal of Econometrics, Elsevier, vol. 235(2), pages 2195-2217.
    15. Rahul Singh, 2021. "Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimension," Papers 2102.11076, arXiv.org, revised Jul 2024.
    16. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul, 2017. "Confidence bands for coefficients in high dimensional linear models with error-in-variables," CeMMAP working papers 22/17, Institute for Fiscal Studies.
    17. Yanqin Fan & Fang Han & Wei Li & Xiao-Hua Zhou, 2019. "On rank estimators in increasing dimensions," Papers 1908.05255, arXiv.org.
    18. Jiaying Gu & Stanislav Volgushev, 2018. "Panel Data Quantile Regression with Grouped Fixed Effects," Papers 1801.05041, arXiv.org, revised Aug 2018.
    19. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers CWP59/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Doğan, Osman & Taşpınar, Süleyman & Bera, Anil K., 2021. "A Bayesian robust chi-squared test for testing simple hypotheses," Journal of Econometrics, Elsevier, vol. 222(2), pages 933-958.
    21. Victor Chernozhukov & Chris Hansen & Martin Spindler, 2016. "High-Dimensional Metrics in R," Papers 1603.01700, arXiv.org, revised Aug 2016.
    22. Victor Chernozhukov & Whitney K Newey & Rahul Singh, 2018. "Automatic Debiased Machine Learning of Causal and Structural Effects," Papers 1809.05224, arXiv.org, revised Oct 2022.
    23. Zhentao Shi & Jingyi Huang, 2019. "Forward-Selected Panel Data Approach for Program Evaluation," Papers 1908.05894, arXiv.org, revised Apr 2021.
    24. Matthew Backus & Sida Peng, 2019. "On Testing Continuity and the Detection of Failures," NBER Working Papers 26016, National Bureau of Economic Research, Inc.

  100. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers 70/13, Institute for Fiscal Studies.

    Cited by:

    1. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, 2016. "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
    2. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    3. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2016. "Post-Selection Inference for Generalized Linear Models With Many Controls," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 606-619, October.
    4. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," Papers 1501.03185, arXiv.org.
    5. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2016. "Valid post-selection and post-regularization inference: An elementary, general approach," CeMMAP working papers CWP36/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2014. "Uniform post selection inference for LAD regression and other Z-estimation problems," CeMMAP working papers 51/14, Institute for Fiscal Studies.
    7. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Ivan Fernandez-Val, 2016. "Conditional quantile processes based on series or many regressors," CeMMAP working papers 46/16, Institute for Fiscal Studies.
    8. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Uniform post selection inference for LAD regression models," CeMMAP working papers 24/13, Institute for Fiscal Studies.
    9. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2013. "Honest confidence regions for a regression parameter in logistic regression with a large number of controls," CeMMAP working papers 67/13, Institute for Fiscal Studies.
    11. Rong Jiang & Mengxian Sun, 2022. "Single-index composite quantile regression for ultra-high-dimensional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 443-460, June.

  101. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Papers 1312.7186, arXiv.org, revised Jun 2016.

    Cited by:

    1. Park, Seyoung & Lee, Eun Ryung, 2021. "Hypothesis testing of varying coefficients for regional quantiles," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    2. Man, Rebeka & Tan, Kean Ming & Wang, Zian & Zhou, Wen-Xin, 2024. "Retire: Robust expectile regression in high dimensions," Journal of Econometrics, Elsevier, vol. 239(2).
    3. Han, Dongxiao & Huang, Jian & Lin, Yuanyuan & Shen, Guohao, 2022. "Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 230(2), pages 416-431.
    4. Chen, Le-Yu & Lee, Sokbae, 2023. "Sparse quantile regression," Journal of Econometrics, Elsevier, vol. 235(2), pages 2195-2217.
    5. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2017. "Non-separable Models with High-dimensional Data," Economics and Statistics Working Papers 15-2017, Singapore Management University, School of Economics.
    6. Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022. "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers 2211.07903, arXiv.org.
    7. Amel, Azzi & Ali, Laksaci & Elias, Ould Saïd, 2022. "On the robustification of the kernel estimator of the functional modal regression," Statistics & Probability Letters, Elsevier, vol. 181(C).
    8. Algo Carè & Simone Garatti & Marco C. Campi, 2017. "A coverage theory for least squares," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1367-1389, November.
    9. Su, Miaomiao & Wang, Qihua, 2022. "A convex programming solution based debiased estimator for quantile with missing response and high-dimensional covariables," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).

  102. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Inference on causal and structural parameters using many moment inequalities," Papers 1312.7614, arXiv.org, revised Oct 2018.

    Cited by:

    1. Steven T Berry & Giovanni Compiani, 2023. "An Instrumental Variable Approach to Dynamic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(4), pages 1724-1758.
    2. Vira Semenova & Matt Goldman & Victor Chernozhukov & Matt Taddy, 2023. "Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 471-510, May.
    3. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
    4. Denni Tommasi & Lina Zhang, 2020. "Bounding Program Benefits When Participation is Misreported," Monash Econometrics and Business Statistics Working Papers 24/20, Monash University, Department of Econometrics and Business Statistics.
    5. D'Haultfoeuille, Xavier & Gaillac, Christophe & Maurel, Arnaud, 2018. "Rationalizing Rational Expectations? Tests and Deviations," IZA Discussion Papers 11989, Institute of Labor Economics (IZA).
    6. Lixiong Li & Marc Henry, 2022. "Finite Sample Inference in Incomplete Models," Papers 2204.00473, arXiv.org, revised Apr 2024.
    7. Parker, Thomas, 2019. "Asymptotic inference for the constrained quantile regression process," Journal of Econometrics, Elsevier, vol. 213(1), pages 174-189.
    8. Ryo Okui, 2021. "A moment inequality approach to statistical inference for rankings," The Japanese Economic Review, Springer, vol. 72(2), pages 169-184, April.
    9. Han Liu & Yongjing Wang & Haiyan Song & Ying Liu, 2023. "Measuring tourism demand nowcasting performance using a monotonicity test," Tourism Economics, , vol. 29(5), pages 1302-1327, August.
    10. Chesher, Andrew & Kim, Dongwoo & Rosen, Adam M., 2023. "IV methods for Tobit models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1700-1724.
    11. , 2023. "Price Competition and Endogenous Product Choice in Networks: Evidence from the US airline Industry," Working Papers 950, Queen Mary University of London, School of Economics and Finance.
    12. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2019. "Improved Central Limit Theorem and bootstrap approximations in high dimensions," Papers 1912.10529, arXiv.org, revised May 2022.
    13. Robert Adamek & Stephan Smeekes & Ines Wilms, 2020. "Lasso Inference for High-Dimensional Time Series," Papers 2007.10952, arXiv.org, revised Sep 2022.
    14. Rami V. Tabri & Christopher D. Walker, 2020. "Inference for Moment Inequalities: A Constrained Moment Selection Procedure," Papers 2008.09021, arXiv.org, revised Aug 2020.
    15. Feng, Long & Lan, Wei & Liu, Binghui & Ma, Yanyuan, 2022. "High-dimensional test for alpha in linear factor pricing models with sparse alternatives," Journal of Econometrics, Elsevier, vol. 229(1), pages 152-175.
    16. Vira Semenova, 2020. "Generalized Lee Bounds," Papers 2008.12720, arXiv.org, revised Feb 2023.
    17. Yu, Xiufan & Yao, Jiawei & Xue, Lingzhou, 2024. "Power enhancement for testing multi-factor asset pricing models via Fisher’s method," Journal of Econometrics, Elsevier, vol. 239(2).
    18. Francesca Molinari, 2020. "Microeconometrics with Partial Identi?cation," CeMMAP working papers CWP15/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. Timmermann, Allan & Qu, Ritong & Zhu, Yinchu, 2019. "Do Any Economists Have Superior Forecasting Skills?," CEPR Discussion Papers 14112, C.E.P.R. Discussion Papers.
    20. Jinyuan Chang & Qing Jiang & Xiaofeng Shao, 2022. "Testing the martingale difference hypothesis in high dimension," Papers 2209.04770, arXiv.org, revised Sep 2022.
    21. Gregory Fletcher Cox, 2024. "A Simple and Adaptive Confidence Interval when Nuisance Parameters Satisfy an Inequality," Papers 2409.09962, arXiv.org.
    22. Wenlong Ji & Lihua Lei & Asher Spector, 2023. "Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects," Papers 2310.08115, arXiv.org, revised Nov 2024.
    23. Kirill Ponomarev & Vira Semenova, 2024. "On the Lower Confidence Band for the Optimal Welfare," Papers 2410.07443, arXiv.org, revised Oct 2024.
    24. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    25. Shosei Sakaguchi, 2024. "Partial identification and inference in duration models with endogenous censoring," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 308-326, March.
    26. Likai Chen & Georg Keilbar & Liangjun Su & Weining Wang, 2023. "Inference on many jumps in nonparametric panel regression models," Papers 2312.01162, arXiv.org, revised Aug 2024.
    27. Evan K. Rose & Yotam Shem-Tov, 2021. "On Recoding Ordered Treatments as Binary Indicators," Papers 2111.12258, arXiv.org, revised Mar 2024.
    28. Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Robust inference in deconvolution," Quantitative Economics, Econometric Society, vol. 12(1), pages 109-142, January.
    29. Nick Koning & Paul Bekker, 2019. "Exact Testing of Many Moment Inequalities Against Multiple Violations," Papers 1904.12775, arXiv.org, revised Jun 2020.
    30. He, Yi & Jaidee, Sombut & Gao, Jiti, 2023. "Most powerful test against a sequence of high dimensional local alternatives," Journal of Econometrics, Elsevier, vol. 234(1), pages 151-177.
    31. Yi He & Sombut Jaidee & Jiti Gao, 2020. "Most Powerful Test against High Dimensional Free Alternatives," Monash Econometrics and Business Statistics Working Papers 13/20, Monash University, Department of Econometrics and Business Statistics.
    32. Jinyong Hahn & Guido Kuersteiner & Andres Santos & Wavid Willigrod, 2024. "Overidentification in Shift-Share Designs," Papers 2404.17049, arXiv.org.

  103. Victor Chernozhukov & Christian Hansen, 2013. "Quantile Models with Endogeneity," Papers 1303.7050, arXiv.org.

    Cited by:

    1. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey & Sami Stouli & Francis Vella, 2017. "Semiparametric estimation of structural functions in nonseparable triangular models," CeMMAP working papers CWP48/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Han, Jidong & Popkowski Leszczyc, Peter T.L. & Zhang, Zelin, 2021. "Empirical Analyses of Nonlinear Effects of Reserve Prices on Ending Prices in Online Auctions," Journal of Interactive Marketing, Elsevier, vol. 54(C), pages 86-102.
    3. Fusejima, Koki, 2024. "Identification of multi-valued treatment effects with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 238(1).
    4. Simone Balestra & Uschi Backes-Gellner, 2014. "Heterogeneous effects of pupil-to-teacher ratio policies - A look at class size reduction and teacher aide," Economics of Education Working Paper Series 0102, University of Zurich, Department of Business Administration (IBW), revised Apr 2017.
    5. Blaise Melly und Kaspar W thrich, 2016. "Local quantile treatment effects," Diskussionsschriften dp1605, Universitaet Bern, Departement Volkswirtschaft.
    6. Clarke, Damian & Llorca-Jaña, Manuel & Pailañir, Daniel, 2021. "The Use of Quantile Methods in Economic History," IZA Discussion Papers 14659, Institute of Labor Economics (IZA).
    7. Callaway, Brantly & Li, Tong & Oka, Tatsushi, 2018. "Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods," Journal of Econometrics, Elsevier, vol. 206(2), pages 395-413.
    8. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    9. Chen, Xiaohong & Pouzo, Demian & Powell, James L., 2019. "Penalized sieve GEL for weighted average derivatives of nonparametric quantile IV regressions," Journal of Econometrics, Elsevier, vol. 213(1), pages 30-53.
    10. Bakouan, Pousseni & Sawadogo, Relwendé, 2024. "BioTrade and income inequality: Does frontier technology readiness matter?," Structural Change and Economic Dynamics, Elsevier, vol. 70(C), pages 650-665.
    11. Shawkat Hammoudeh & Amine Lahiani & Duc Khuong Nguyen & Ricardo M. Sousa, 2014. "Energy prices and CO2 emission allowance prices: A quantile regression approach," NIPE Working Papers 06/2014, NIPE - Universidade do Minho.
    12. Dilshad Ahmad & Muhammad Afzal, 2022. "Impact of violent conflicts and environmental hazards on pastoral sustainable development in Punjab, Pakistan," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(9), pages 11260-11281, September.
    13. Simone Balestra & Uschi Backes-Gellner, 2013. "Heterogeneous Returns to Education Over Wage Distribution: Who Profits the Most?," Economics of Education Working Paper Series 0091, University of Zurich, Department of Business Administration (IBW), revised Dec 2013.
    14. Pengzhou Wu & Kenji Fukumizu, 2021. "$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap," Papers 2110.05225, arXiv.org.
    15. Christina Christou & Ruthira Naraidoo & Rangan Gupta & Won Joong Kim, 2017. "Monetary Policy Reaction Functions of the TICKs: A Quantile Regression Approach," Working Papers 201738, University of Pretoria, Department of Economics.
    16. Michael Chletsos & Stelios Roupakias, 2020. "Education and wage inequality before and during the fiscal crisis: A quantile regression analysis for Greece 2006–2016," Review of Development Economics, Wiley Blackwell, vol. 24(4), pages 1333-1364, November.
    17. Valizadeh, Pourya & Smith, Travis A., 2017. "How Did the American Recovery and Reinvestment Act (ARRA) Impact the Material Well-being of SNAP Participants? A Distributional Approach," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258496, Agricultural and Applied Economics Association.
    18. Nahid Farnaz, 2023. "Does Financial Development Relieve or Exacerbate Income Inequality? A Quantile Regression Approach," Economics Discussion Paper Series 2311, Economics, The University of Manchester.
    19. Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," Journal of Econometrics, Elsevier, vol. 210(2), pages 219-235.
    20. Paul. B. Kenfac Dongmezo & P. N. Mwita & I. R. Kamga Tchwaket, 2018. "Distributive and Quantile Treatment Effects: Imputation Based Estimators Approach," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 7(2), pages 1-3.
    21. Kaspar W thrich, 2014. "A Comparison of two Quantile Models with Endogeneity," Diskussionsschriften dp1408, Universitaet Bern, Departement Volkswirtschaft.
    22. Ghosh, Pallab Kumar, 2014. "The contribution of human capital variables to changes in the wage distribution function," Labour Economics, Elsevier, vol. 28(C), pages 58-69.
    23. Saifullahi Sani Ibrahim & Huseyin Ozdeser & Behiye Cavusoglu, 2020. "Testing the impact of environmental hazards and violent conflicts on sustainable pastoral development: micro-level evidence from Nigeria," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4169-4190, June.
    24. Apergis, Nicholas & Christou, Christina, 2015. "The behaviour of the bank lending channel when interest rates approach the zero lower bound: Evidence from quantile regressions," Economic Modelling, Elsevier, vol. 49(C), pages 296-307.
    25. Mark Stater & Jeffrey B Wenger, 2017. "The Immediate Hardship of Unemployment: Evidence from the US Unemployment Insurance System," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 43(1), pages 17-36, January.
    26. Pourya Valizadeh & Travis A Smith, 2020. "How Did The American Recovery and Reinvestment Act Affect the Material Well‐Being of SNAP Participants? A Distributional Approach," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(3), pages 455-476, September.
    27. Hiroaki Kaido & Kaspar Wüthrich, 2018. "Decentralization estimators for instrumental variable quantile regression models," CeMMAP working papers CWP72/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    28. Bai Huang & Tae-Hwy Lee & Aman Ullah, 2017. "A combined estimator of regression models with measurement errors," Indian Economic Review, Springer, vol. 52(1), pages 73-91, December.
    29. Bola Amoke Awotide & Adebayo Ogunniyi & Kehinde Oluseyi Olagunju & Lateef Olalekan Bello & Amadou Youssouf Coulibaly & Alexander Nimo Wiredu & Bourémo Kone & Aly Ahamadou & Victor Manyong & Tahirou Ab, 2022. "Evaluating the Heterogeneous Impacts of Adoption of Climate-Smart Agricultural Technologies on Rural Households’ Welfare in Mali," Agriculture, MDPI, vol. 12(11), pages 1-16, November.
    30. Kaspar W thrich, 2015. "Semiparametric estimation of quantile treatment effects with endogeneity," Diskussionsschriften dp1509, Universitaet Bern, Departement Volkswirtschaft.
    31. David Powell, 2020. "Quantile Treatment Effects in the Presence of Covariates," The Review of Economics and Statistics, MIT Press, vol. 102(5), pages 994-1005, December.
    32. Myoung‐jae Lee & Jin‐young Choi, 2022. "Finding mover–stayer quantile difference due to unobservables using quantile selection corrections," Bulletin of Economic Research, Wiley Blackwell, vol. 74(3), pages 704-721, July.
    33. Saifullahi Sani Ibrahim & Haruna Mohammad Aliero, 2020. "Testing the impact of financial inclusion on income convergence: Empirical evidence from Nigeria," African Development Review, African Development Bank, vol. 32(1), pages 42-54, March.
    34. Santiago Pereda Fernández, 2016. "Estimation of counterfactual distributions with a continuous endogenous treatment," Temi di discussione (Economic working papers) 1053, Bank of Italy, Economic Research and International Relations Area.

  104. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2013. "Honest confidence regions for a regression parameter in logistic regression with a large number of controls," CeMMAP working papers 67/13, Institute for Fiscal Studies.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    3. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers 62/13, Institute for Fiscal Studies.
    4. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    5. Christian Hansen & Yuan Liao, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," Papers 1611.09420, arXiv.org, revised Dec 2016.
    6. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    7. Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers 57/13, Institute for Fiscal Studies.
    8. Max H. Farrell, 2013. "Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations," Papers 1309.4686, arXiv.org, revised Feb 2018.
    9. Yichen Gao & Yu Zhang & Ximing Wu, 2015. "Penalized exponential series estimation of copula densities with an application to intergenerational dependence of body mass index," Empirical Economics, Springer, vol. 48(1), pages 61-81, February.
    10. Sasaki, Yuya & Ura, Takuya, 2023. "Estimation and inference for policy relevant treatment effects," Journal of Econometrics, Elsevier, vol. 234(2), pages 394-450.
    11. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers CWP70/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Stijn Vansteelandt & Oliver Dukes, 2022. "Assumption‐lean inference for generalised linear model parameters," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 657-685, July.
    13. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers CWP59/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. van de Geer, Sara, 2016. "Worst possible sub-directions in high-dimensional models," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 248-260.
    15. Barbara Felderer & Jannis Kueck & Martin Spindler, 2021. "Big Data meets Causal Survey Research: Understanding Nonresponse in the Recruitment of a Mixed-mode Online Panel," Papers 2102.08994, arXiv.org.
    16. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"," Papers 1305.6099, arXiv.org, revised Jun 2013.

  105. Victor Chernozhukov & Ivan Fernandez-Val & Stefan Hoderlein & Hajo Holzmann & Whitney Newey, 2013. "Nonparametric Identification in Panels using Quantiles," Papers 1312.4094, arXiv.org, revised Aug 2014.

    Cited by:

    1. Botosaru, Irene & Muris, Chris & Pendakur, Krishna, 2023. "Identification of time-varying transformation models with fixed effects, with an application to unobserved heterogeneity in resource shares," Journal of Econometrics, Elsevier, vol. 232(2), pages 576-597.
    2. Ghanem, Dalia, 2017. "Testing identifying assumptions in nonseparable panel data models," Journal of Econometrics, Elsevier, vol. 197(2), pages 202-217.
    3. Bryan S. Graham & Jinyong Hahn & Alexandre Poirier & James L. Powell, 2016. "A quantile correlated random coefficients panel data model," CeMMAP working papers 34/16, Institute for Fiscal Studies.
    4. Callaway, Brantly & Li, Tong & Oka, Tatsushi, 2018. "Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods," Journal of Econometrics, Elsevier, vol. 206(2), pages 395-413.
    5. Matthew Harding & Carlos Lamarche, 2018. "A Panel Quantile Approach to Attrition Bias in Big Data: Evidence from a Randomized Experiment," Papers 1808.03364, arXiv.org.
    6. Demian Pouzo & Zacharias Psaradakis & Martin Sola, 2022. "Maximum Likelihood Estimation in Markov Regime‐Switching Models With Covariate‐Dependent Transition Probabilities," Econometrica, Econometric Society, vol. 90(4), pages 1681-1710, July.
    7. Victor Chernozhukov & Iv'an Fern'andez-Val & Martin Weidner, 2018. "Network and Panel Quantile Effects Via Distribution Regression," Papers 1803.08154, arXiv.org, revised Jun 2020.
    8. Antonio F. Galvao & Jiaying Gu & Stanislav Volgushev, 2018. "On the Unbiased Asymptotic Normality of Quantile Regression with Fixed Effects," Papers 1807.11863, arXiv.org, revised Feb 2020.
    9. Manuel Arellano & Stéphane Bonhomme, 2015. "Nonlinear panel data estimation via quantile regressions," CeMMAP working papers 40/15, Institute for Fiscal Studies.
    10. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey, 2017. "Nonseparable multinomial choice models in cross-section and panel data," CeMMAP working papers 33/17, Institute for Fiscal Studies.
    11. Stefan Hoderlein & Hajo Holzmann & Maximilian Kasy & Alexander Meister, 2015. "Erratum regarding “Instrumental variables with unrestricted heterogeneity and continuous treatment”," Boston College Working Papers in Economics 896, Boston College Department of Economics, revised 01 Feb 2016.
    12. Cizek, Pavel & Sadikoglu, Serhan, 2022. "Nonseparable Panel Models with Index Structure and Correlated Random Effects," Discussion Paper 2022-009, Tilburg University, Center for Economic Research.
    13. Bryan S. Graham & Jinyong Hahn & Alexandre Poirier & James L. Powell, 2015. "Quantile Regression with Panel Data," NBER Working Papers 21034, National Bureau of Economic Research, Inc.
    14. Oliver Linton & Ji-Liang Shiu, 2018. "Semiparametric nonlinear panel data models with measurement error," CeMMAP working papers CWP09/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. Irene Botosaru & Chris Muris, 2017. "Binarization for panel models with fixed effects," CeMMAP working papers CWP31/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Demian Pouzo & Zacharias Psaradakis & Martin Sola, 2016. "Maximum Likelihood Estimation in Possibly Misspeci ed Dynamic Models with Time-Inhomogeneous Markov Regimes," Department of Economics Working Papers 2016_04, Universidad Torcuato Di Tella.
    17. Jiaying Gu & Stanislav Volgushev, 2018. "Panel Data Quantile Regression with Grouped Fixed Effects," Papers 1801.05041, arXiv.org, revised Aug 2018.
    18. Čížek, Pavel & Lei, Jinghua, 2018. "Identification and estimation of nonseparable single-index models in panel data with correlated random effects," Journal of Econometrics, Elsevier, vol. 203(1), pages 113-128.
    19. Victor Chernozhukov & Iván Fernández‐Val & Ye Luo, 2018. "The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages," Econometrica, Econometric Society, vol. 86(6), pages 1911-1938, November.
    20. Liang Chen, 2019. "Nonparametric Quantile Regressions for Panel Data Models with Large T," Papers 1911.01824, arXiv.org, revised Sep 2020.
    21. Galvao, Antonio F. & Kato, Kengo, 2016. "Smoothed quantile regression for panel data," Journal of Econometrics, Elsevier, vol. 193(1), pages 92-112.
    22. Ishihara, Takuya, 2020. "Identification and estimation of time-varying nonseparable panel data models without stayers," Journal of Econometrics, Elsevier, vol. 215(1), pages 184-208.
    23. D’Haultfœuille, Xavier & Hoderlein, Stefan & Sasaki, Yuya, 2023. "Nonparametric difference-in-differences in repeated cross-sections with continuous treatments," Journal of Econometrics, Elsevier, vol. 234(2), pages 664-690.
    24. Takuya Ishihara, 2020. "Panel Data Quantile Regression for Treatment Effect Models," Papers 2001.04324, arXiv.org, revised Nov 2021.
    25. Yuya Sasaki & Takuya Ura, 2021. "Slow Movers in Panel Data," Papers 2110.12041, arXiv.org.
    26. Irene Botosaru & Chris Muris & Krishna Pendakur, 2020. "Intertemporal Collective Household Models: Identification in Short Panels with Unobserved Heterogeneity in Resource Shares," Department of Economics Working Papers 2020-09, McMaster University.
    27. Gu, Jiaying & Volgushev, Stanislav, 2019. "Panel data quantile regression with grouped fixed effects," Journal of Econometrics, Elsevier, vol. 213(1), pages 68-91.
    28. Irene Botosaru & Chris Muris, 2022. "Identification of time-varying counterfactual parameters in nonlinear panel models," Papers 2212.09193, arXiv.org, revised Nov 2023.

  106. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2013. "Post-Selection Inference for Generalized Linear Models with Many Controls," Papers 1304.3969, arXiv.org, revised Mar 2016.

    Cited by:

    1. Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," CEPR Discussion Papers 13430, C.E.P.R. Discussion Papers.
    2. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    3. Liqian Cai & Arnab Bhattacharjee & Roger Calantone & Taps Maiti, 2019. "Variable Selection with Spatially Autoregressive Errors: A Generalized Moments LASSO Estimator," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 146-200, September.
    4. Backes-Gellner, Uschi & Herz, Holger & Kosfeld, Michael & Oswald, Yvonne, 2018. "Do Preferences and Biases Predict Life Outcomes? Evidence from Education and Labor Market Entry Decisions," IZA Discussion Papers 11288, Institute of Labor Economics (IZA).
    5. Jelena Bradic & Stefan Wager & Yinchu Zhu, 2019. "Sparsity Double Robust Inference of Average Treatment Effects," Papers 1905.00744, arXiv.org.
    6. Daniel Garcia & Juha Tolvanen & Alexander K. Wagner, 2022. "Demand Estimation Using Managerial Responses to Automated Price Recommendations," Management Science, INFORMS, vol. 68(11), pages 7918-7939, November.
    7. Sung Jae Jun & Sokbae Lee, 2020. "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," Papers 2004.08318, arXiv.org, revised Oct 2023.
    8. Chaohua Dong & Jiti Gao & Oliver Linton, 2018. "High dimensional semiparametric moment restriction models," CeMMAP working papers CWP04/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    10. Nebojša Stojčić & Matija Matić, 2023. "A Journey Toward Global Value Chain Upgrading: Exploring the Transition from Backward to Forward Integration," EFZG Working Papers Series 2308, Faculty of Economics and Business, University of Zagreb.
    11. Woodruff, Christopher & Menzel, Andreas, 2019. "Gender Wage Gaps and Worker Mobility: Evidence from the Garment Sector in Bangladesh," CEPR Discussion Papers 13577, C.E.P.R. Discussion Papers.
    12. Alex Coad & Stjepan Srhoj, 2020. "Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms," Small Business Economics, Springer, vol. 55(3), pages 541-565, October.
    13. Yukun Ma, 2023. "Identification-robust inference for the LATE with high-dimensional covariates," Papers 2302.09756, arXiv.org, revised Nov 2023.
    14. Ana Costa-Ramón & Ursina Schaede & Michaela Slotwinski & Anne Ardila Brenøe, 2024. "(Not) thinking about the future: inattention and maternal labor supply," ECON - Working Papers 452, Department of Economics - University of Zurich.
    15. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    16. Helios Herrera & Massimo Morelli & Salvatore Nunnari, 2020. "A Theory of Power Wars," Working Papers 669, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    17. Robert Brooks & Brandon N. Cline & Pavel Teterin & Yu You, 2022. "The information in global interest rate futures contracts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(6), pages 1135-1166, June.
    18. Ethan X. Fang & Yang Ning & Han Liu, 2017. "Testing and confidence intervals for high dimensional proportional hazards models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1415-1437, November.
    19. Joann Jasiak & Purevdorj Tuvaandorj, 2023. "Penalized Likelihood Inference with Survey Data," Papers 2304.07855, arXiv.org.
    20. Shengfei Tang & Yanmei Shi & Qi Zhang, 2023. "Bias-Corrected Inference of High-Dimensional Generalized Linear Models," Mathematics, MDPI, vol. 11(4), pages 1-14, February.
    21. Dong, Chaohua & Gao, Jiti & Linton, Oliver, 2023. "High dimensional semiparametric moment restriction models," Journal of Econometrics, Elsevier, vol. 232(2), pages 320-345.
    22. Harold D. Chiang, 2019. "Many Average Partial Effects: with an Application to Text Regression," 2019 Papers pch1836, Job Market Papers.
    23. Ofori, Isaac K. & Quaidoo, Christopher & Ofori, Pamela E., 2021. "What Drives Financial Sector Development in Africa? Insights from Machine Learning," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue forthcomi.
    24. Sung Jae Jun & Sokbae (Simon) Lee, 2020. "Causal inference in case-control studies," CeMMAP working papers CWP19/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    25. Rocco Macchiavello & Andreas Menzel & Atonu Rabbani & Christopher Woodruff, 2020. "Challenges of Change: An Experiment Promoting Women to Managerial Roles in the Bangladeshi Garment Sector," NBER Working Papers 27606, National Bureau of Economic Research, Inc.
    26. Sung Jae Jun & Sokbae Lee, 2022. "Average Adjusted Association: Efficient Estimation with High Dimensional Confounders," Papers 2205.14048, arXiv.org, revised Apr 2023.
    27. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2017. "Non-separable Models with High-dimensional Data," Economics and Statistics Working Papers 15-2017, Singapore Management University, School of Economics.
    28. Harold D Chiang & Yukun Ma & Joel Rodrigue & Yuya Sasaki, 2021. "Dyadic double/debiased machine learning for analyzing determinants of free trade agreements," Papers 2110.04365, arXiv.org, revised Dec 2022.
    29. Vira Semenova, 2020. "Generalized Lee Bounds," Papers 2008.12720, arXiv.org, revised Feb 2023.
    30. Byol Kim & Song Liu & Mladen Kolar, 2021. "Two‐sample inference for high‐dimensional Markov networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 939-962, November.
    31. Yang Ning & Sida Peng & Jing Tao, 2020. "Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data," Papers 2009.03151, arXiv.org.
    32. Falco J. Bargagli-Dtoffi & Massimo Riccaboni & Armando Rungi, 2020. "Machine Learning for Zombie Hunting. Firms Failures and Financial Constraints," Working Papers 01/2020, IMT School for Advanced Studies Lucca, revised Jun 2020.
    33. Joann Jasiak & Peter MacKenzie & Purevdorj Tuvaandorj, 2023. "Digital Divide: Empirical Study of CIUS 2020," Papers 2301.07855, arXiv.org, revised Oct 2024.
    34. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
    35. Menzel, Andreas, 2021. "Knowledge exchange and productivity spill-overs in Bangladeshi garment factories," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 721-746.
    36. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
    37. Sophie Brana & Dalila Chenaf-Nicet & Delphine Lahet, 2023. "Drivers of cross-border bank claims: The role of foreign-owned banks in emerging countries," Working Papers 2023.06, International Network for Economic Research - INFER.
    38. Falco J. Bargagli-Stoffi & Fabio Incerti & Massimo Riccaboni & Armando Rungi, 2023. "Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values," Papers 2306.08165, arXiv.org.
    39. Oliver Dukes & Vahe Avagyan & Stijn Vansteelandt, 2020. "Doubly robust tests of exposure effects under high‐dimensional confounding," Biometrics, The International Biometric Society, vol. 76(4), pages 1190-1200, December.
    40. Victor Chernozhukov & Vira Semenova, 2018. "Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions," CeMMAP working papers CWP40/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    41. Barbara Felderer & Jannis Kueck & Martin Spindler, 2021. "Big Data meets Causal Survey Research: Understanding Nonresponse in the Recruitment of a Mixed-mode Online Panel," Papers 2102.08994, arXiv.org.
    42. Menzel, Andreas & Woodruff, Christopher, 2021. "Gender wage gaps and worker mobility: Evidence from the garment sector in Bangladesh," Labour Economics, Elsevier, vol. 71(C).
    43. Kelly Van Lancker & Oliver Dukes & Stijn Vansteelandt, 2023. "Ensuring valid inference for Cox hazard ratios after variable selection," Biometrics, The International Biometric Society, vol. 79(4), pages 3096-3110, December.
    44. Masahiro Kato, 2024. "Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects," Papers 2403.03240, arXiv.org.

  107. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Uniform post selection inference for LAD regression models," CeMMAP working papers 24/13, Institute for Fiscal Studies.

    Cited by:

    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    2. Victor Chernozhukov & Mert Demirer & Esther Duflo & Ivan Fernandez-Val, 2017. "Generic machine learning inference on heterogenous treatment effects in randomized experiments," CeMMAP working papers CWP61/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers 62/13, Institute for Fiscal Studies.
    4. Christian Hansen & Yuan Liao, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," Papers 1611.09420, arXiv.org, revised Dec 2016.
    5. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    6. Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers 57/13, Institute for Fiscal Studies.
    7. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," Papers 1501.03185, arXiv.org.
    8. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2016. "Valid post-selection and post-regularization inference: An elementary, general approach," CeMMAP working papers CWP36/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Victor Chernozhukov & Whitney Newey & Rahul Singh, 2018. "De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers," Papers 1802.08667, arXiv.org, revised Oct 2022.
    10. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iv'an Fern'andez-Val, 2017. "Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," Papers 1712.04802, arXiv.org, revised Oct 2023.
    11. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul, 2017. "Confidence bands for coefficients in high dimensional linear models with error-in-variables," CeMMAP working papers 22/17, Institute for Fiscal Studies.
    12. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers CWP70/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers CWP59/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Uniform post selection inference for LAD regression models," CeMMAP working papers 24/13, Institute for Fiscal Studies.
    15. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2013. "Honest confidence regions for a regression parameter in logistic regression with a large number of controls," CeMMAP working papers 67/13, Institute for Fiscal Studies.
    17. Victor Chernozhukov & Whitney K. Newey & James Robins, 2018. "Double/de-biased machine learning using regularized Riesz representers," CeMMAP working papers CWP15/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"," Papers 1305.6099, arXiv.org, revised Jun 2013.

  108. Alexandre Belloni & Victor Chernozhukov & Ivan Fern'andez-Val & Christian Hansen, 2013. "Program Evaluation and Causal Inference with High-Dimensional Data," Papers 1311.2645, arXiv.org, revised Jan 2018.

    Cited by:

    1. Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," CEPR Discussion Papers 13430, C.E.P.R. Discussion Papers.
    2. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    3. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    4. Yamin Ahmad & Adam Check & Ming Chien Lo, 2024. "Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2139-2173, June.
    5. MIYAKAWA Daisuke, 2019. "Shocks to Supply Chain Networks and Firm Dynamics: An Application of Double Machine Learning," Discussion papers 19100, Research Institute of Economy, Trade and Industry (RIETI).
    6. Mazzocchi, Mario & Capacci, Sara & Biondi, Beatrice, 2022. "Causal inference on the impact of nutrition policies using observational data," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 11(1), April.
    7. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, 2016. "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
    9. Neng-Chieh Chang, 2020. "The Mode Treatment Effect," Papers 2007.11606, arXiv.org.
    10. Pedro H. C. Sant'Anna & Jun B. Zhao, 2018. "Doubly Robust Difference-in-Differences Estimators," Papers 1812.01723, arXiv.org, revised May 2020.
    11. Ying-Ying Lee & Chu-An Liu, 2024. "Lee Bounds with a Continuous Treatment in Sample Selection," Papers 2411.04312, arXiv.org.
    12. Huber, Martin, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    13. Hartley, Robert Paul & Lamarche, Carlos, 2017. "Behavioral Responses and Welfare Reform: Evidence from a Randomized Experiment," IZA Discussion Papers 10905, Institute of Labor Economics (IZA).
    14. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment," DETU Working Papers 1804, Department of Economics, Temple University.
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    19. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    20. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org, revised Nov 2024.
    21. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    22. Lechner, Michael & Knaus, Michael C. & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," CEPR Discussion Papers 13402, C.E.P.R. Discussion Papers.
    23. Vira Semenova, 2023. "Aggregated Intersection Bounds and Aggregated Minimax Values," Papers 2303.00982, arXiv.org, revised Jun 2024.
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    25. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
    26. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
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    28. Giuseppe de Luca & Jan Magnus & Franco Peracchi, 2017. "Weighted-Average Least Squares Estimation of Generalized Linear Models," Tinbergen Institute Discussion Papers 17-029/III, Tinbergen Institute.
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    31. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
    32. Denis Fougère & Nicolas Jacquemet, 2020. "Policy Evaluation Using Causal Inference Methods," SciencePo Working papers Main hal-03455978, HAL.
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    34. Michael C. Knaus, 2018. "A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills," Papers 1805.10300, arXiv.org, revised Jan 2019.
    35. Knaus, Michael C., 2020. "Double Machine Learning Based Program Evaluation under Unconfoundedness," IZA Discussion Papers 13051, Institute of Labor Economics (IZA).
    36. Cattaneo, Matias D & Jansson, Michael & Newey, Whitney K, 2018. "Inference in Linear Regression Models with Many Covariates and Heteroscedasticity," Department of Economics, Working Paper Series qt6rp7p9gs, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    37. Whitney Newey & Sami Stouli, 2018. "Control Variables, Discrete Instruments, and Identification of Structural Functions," Bristol Economics Discussion Papers 18/702, School of Economics, University of Bristol, UK.
    38. Yuehao Bai & Jizhou Liu & Azeem M. Shaikh & Max Tabord-Meehan, 2023. "On the Efficiency of Finely Stratified Experiments," Papers 2307.15181, arXiv.org, revised Aug 2024.
    39. Rodney V. Fonseca & Aluísio Pinheiro, 2020. "Wavelet estimation of the dimensionality of curve time series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(5), pages 1175-1204, October.
    40. Victor Quintas-Martinez & Mohammad Taha Bahadori & Eduardo Santiago & Jeff Mu & Dominik Janzing & David Heckerman, 2024. "Multiply-Robust Causal Change Attribution," Papers 2404.08839, arXiv.org, revised Sep 2024.
    41. Francesca Micocci & Armando Rungi, 2021. "Predicting Exporters with Machine Learning," Papers 2107.02512, arXiv.org, revised Sep 2022.
    42. Fernandes, Ana & Huber, Martin & Vaccaro, Giannina, 2020. "Gender Differences in Wage Expectations," FSES Working Papers 516, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    43. Entorf, Horst & Hou, Jia, 2018. "Financial education for the disadvantaged? A review," SAFE Working Paper Series 205, Leibniz Institute for Financial Research SAFE.
    44. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    45. Sant’Anna, Pedro H.C. & Song, Xiaojun, 2019. "Specification tests for the propensity score," Journal of Econometrics, Elsevier, vol. 210(2), pages 379-404.
    46. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
    47. Tymon Sloczynski & Derya Uysal & Jeffrey Wooldridge, 2023. "Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect," Rationality and Competition Discussion Paper Series 424, CRC TRR 190 Rationality and Competition.
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    50. Dylan Brewer & Alyssa Carlson, 2021. "Addressing Sample Selection Bias for Machine Learning Methods," Working Papers 2102, Department of Economics, University of Missouri.
    51. Sven Klaassen & Jannis Kueck & Martin Spindler, 2017. "Transformation Models in High-Dimensions," Papers 1712.07364, arXiv.org.
    52. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    53. Denis Fougère & Nicolas Jacquemet, 2019. "Causal Inference and Impact Evaluation," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 510-511-5, pages 181-200.
    54. Undral Byambadalai & Tatsushi Oka & Shota Yasui, 2024. "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction," Papers 2407.16037, arXiv.org.
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    56. Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2023. "Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 235(1), pages 166-179.
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    58. Valente, Marica, 2023. "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
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    64. Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
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    66. Johann Pfitzinger, 2021. "An Interpretable Neural Network for Parameter Inference," Papers 2106.05536, arXiv.org.
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    123. Neng-Chieh Chang, 2018. "Semiparametric Difference-in-Differences with Potentially Many Control Variables," Papers 1812.10846, arXiv.org, revised Jan 2019.
    124. Qizhao Chen & Vasilis Syrgkanis & Morgane Austern, 2022. "Debiased Machine Learning without Sample-Splitting for Stable Estimators," Papers 2206.01825, arXiv.org, revised Nov 2022.
    125. Tomasz Olma, 2021. "Nonparametric Estimation of Truncated Conditional Expectation Functions," Papers 2109.06150, arXiv.org.
    126. Herrera, Diego & Cunniff, Shannon & DuPont, Carolyn & Cohen, Benjamin & Gangi, Dakota & Kar, Devyani & Peyronnin Snider, Natalie & Rojas, Victor & Wyerman, Jim & Norriss, Jessie & Mountenot, Marshall, 2019. "Designing an environmental impact bond for wetland restoration in Louisiana," Ecosystem Services, Elsevier, vol. 35(C), pages 260-276.
    127. Barbara Felderer & Jannis Kueck & Martin Spindler, 2021. "Big Data meets Causal Survey Research: Understanding Nonresponse in the Recruitment of a Mixed-mode Online Panel," Papers 2102.08994, arXiv.org.
    128. Biewen, Martin & Fitzenberger, Bernd & Seckler, Matthias, 2020. "Counterfactual quantile decompositions with selection correction taking into account Huber/Melly (2015): An application to the German gender wage gap," Labour Economics, Elsevier, vol. 67(C).
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    130. Narita, Yusuke & Yata, Kohei, 2022. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," CEI Working Paper Series 2021-05, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
    131. Yang, Jui-Chung & Chuang, Hui-Ching & Kuan, Chung-Ming, 2020. "Double machine learning with gradient boosting and its application to the Big N audit quality effect," Journal of Econometrics, Elsevier, vol. 216(1), pages 268-283.
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    1. Aline Bütikofer & Rita Ginja & Fanny Landaud & Katrine Løken, 2020. "School Selectivity, Peers, and Mental Health," Working Papers 2020-074, Human Capital and Economic Opportunity Working Group.
    2. Rao, Manaswini & Shenoy, Ashish, 2023. "Got (clean) milk? Organization, incentives, and management in Indian dairy cooperatives," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 708-722.
    3. Toru Kitagawa & Chris Muris, 2015. "Model averaging in semiparametric estimation of treatment effects," CeMMAP working papers CWP46/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Michael J. Weir & Thomas W. Sproul, 2019. "Identifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment," Sustainability, MDPI, vol. 11(14), pages 1-21, July.
    5. Shukla, Pallavi & Pullabhotla, Hemant K. & Baylis, Kathy, 2022. "Trouble with zero: The limits of subsidizing technology adoption," Journal of Development Economics, Elsevier, vol. 158(C).
    6. Chakravorty, Bhaskar & Arulampalam, Wiji & Bhatiya, Apurav Yash & Imbert, Clement & Rathelot, Roland, 2021. "Can information about jobs improve the effectiveness of vocational training? Experimental evidence from India," CAGE Online Working Paper Series 567, Competitive Advantage in the Global Economy (CAGE).
    7. Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," IZA Discussion Papers 10961, Institute of Labor Economics (IZA).
    8. Alexandre Belloni & Victor Chernozhukov & Christian Hansen & Damian Kozbur, 2014. "Inference in High Dimensional Panel Models with an Application to Gun Control," Papers 1411.6507, arXiv.org.
    9. González, F & Muñoz, P & Prem, M, 2019. "Lost in Transition? The Persistence of Dictatorship Mayors," Documentos de Trabajo 17431, Universidad del Rosario.
    10. Yash Raj Shrestha & Vivianna Fang He & Phanish Puranam & Georg von Krogh, 2021. "Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize?," Organization Science, INFORMS, vol. 32(3), pages 856-880, May.
    11. Teck-Hua Ho & Noah Lim & Sadat Reza & Xiaoyu Xia, 2017. "OM Forum—Causal Inference Models in Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 509-525, October.
    12. Wunsch, Conny & Strittmatter, Anthony, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," CEPR Discussion Papers 15840, C.E.P.R. Discussion Papers.
    13. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2016. "Post-Selection Inference for Generalized Linear Models With Many Controls," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 606-619, October.
    14. Clarke, Damian, 2023. "The Economics of Abortion Policy," IZA Discussion Papers 16395, Institute of Labor Economics (IZA).
    15. Alex Armand & Britta Augsburg & Antonella Bancalari, 2021. "Coordination and the poor maintenance trap: an experiment on public infrastructure in India," IFS Working Papers W21/16, Institute for Fiscal Studies.
    16. John List & Ian Muir & Gregory Sun, 2022. "Using Machine Learning for Efficient Flexible Regression Adjustment in Economic Experiments," Natural Field Experiments 00763, The Field Experiments Website.
    17. Stefano Cabras & J. D. Tena, 2023. "Implicit institutional incentives and individual decisions: Causal inference with deep learning models," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(6), pages 3739-3754, September.
    18. Alexander Kreiss & Christoph Rothe, 2023. "Inference in regression discontinuity designs with high-dimensional covariates," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 105-123.
    19. Kaila, Heidi & Azad, Abul, 2023. "The effects of crime and violence on food insecurity and consumption in Nigeria," Food Policy, Elsevier, vol. 115(C).
    20. Haroon, Maryiam & Said, Farah & Zafar, Mahniya, 2022. "Fostering non-cognitive skills and academic performance: Experimental evidence from women’s-only colleges in Pakistan," Journal of Asian Economics, Elsevier, vol. 81(C).
    21. Miric, Milan & Boudreau, Kevin J. & Jeppesen, Lars Bo, 2019. "Protecting their digital assets: The use of formal & informal appropriability strategies by App developers," Research Policy, Elsevier, vol. 48(8), pages 1-1.
    22. Ji, Chengyuan & Jiang, Junyan & Zhang, Yujin, 2024. "Political trust and government performance in the time of COVID-19," World Development, Elsevier, vol. 176(C).
    23. Jin, Wei & van der Ploeg, Frederick & Zhang, Lin, 2024. "How clean capital slows down disinvestment of carbon-intensive capital in the low-carbon transition," Journal of Economic Dynamics and Control, Elsevier, vol. 162(C).
    24. Helmut Wasserbacher & Martin Spindler, 2024. "Credit Ratings: Heterogeneous Effect on Capital Structure," Papers 2406.18936, arXiv.org.
    25. Kovacs, Roxanne J. & Lagarde, Mylene & Cairns, John, 2022. "Can patients improve the quality of care they receive? Experimental evidence from Senegal," World Development, Elsevier, vol. 150(C).
    26. Matthias Breuer & Harm H. Schütt, 2023. "Accounting for uncertainty: an application of Bayesian methods to accruals models," Review of Accounting Studies, Springer, vol. 28(2), pages 726-768, June.
    27. Byol Kim & Song Liu & Mladen Kolar, 2021. "Two‐sample inference for high‐dimensional Markov networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 939-962, November.
    28. Chakravorty, Bhaskar & Bhatiya, Apurav Yash & Imbert, Clement & Lohnert, Maximilian & Panda, Poonam & Rathelot, Roland, 2022. "Impact of the COVID-19 Crisis on India’s Rural Youth : Evidence from a Panel Survey and an Experiment," The Warwick Economics Research Paper Series (TWERPS) 1419, University of Warwick, Department of Economics.
    29. Travis J. Lybbert & Ashish Shenoy & Tomoé Bourdier & Caitlin Kieran, 2024. "Striving to revive pulses in India with extension, input subsidies, and output price supports†," American Journal of Agricultural Economics, John Wiley & Sons, vol. 106(3), pages 1167-1192, May.
    30. Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
    31. Alex Armand & Britta Augsburg & Antonella Bancalari & Kalyan Kumar Kameshwara, 2023. "Social proximity and misinformation: Experimental evidence from a mobile phone-based campaign in India," IFS Working Papers W23/39, Institute for Fiscal Studies.
    32. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers CWP59/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    33. Ian W. McKeague & Min Qian, 2015. "An Adaptive Resampling Test for Detecting the Presence of Significant Predictors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1422-1433, December.
    34. Martin C. Arnold & Thilo Reinschlussel, 2024. "Bootstrap Adaptive Lasso Solution Path Unit Root Tests," Papers 2409.07859, arXiv.org.
    35. Renée, Laëtitia, 2022. "The long-term effects of financial aid and career education: Evidence from a randomized experiment," CLEF Working Paper Series 46, Canadian Labour Economics Forum (CLEF), University of Waterloo.
    36. Armand, Alex & Augsburg, Britta & Bancalari, Antonella & Ghatak, Maitreesh, 2023. "Public Service Delivery, Exclusion and Externalities: Theory and Experimental Evidence from India," CEPR Discussion Papers 18636, C.E.P.R. Discussion Papers.
    37. Alexandre Belloni & Victor Chernozhukov, 2015. "Comment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1449-1451, December.
    38. Mckenzie,David J. & Sansone,Dario & Mckenzie,David J. & Sansone,Dario, 2017. "Man vs. machine in predicting successful entrepreneurs : evidence from a business plan competition in Nigeria," Policy Research Working Paper Series 8271, The World Bank.
    39. David Cheng & Abhishek Chakrabortty & Ashwin N. Ananthakrishnan & Tianxi Cai, 2020. "Estimating average treatment effects with a double‐index propensity score," Biometrics, The International Biometric Society, vol. 76(3), pages 767-777, September.
    40. Pollack, Adam B. & Kaufmann, Robert K., 2022. "Increasing storm risk, structural defense, and house prices in the Florida Keys," Ecological Economics, Elsevier, vol. 194(C).
    41. Armand, Alex & Augsburg, Britta & Bancalari, Antonella & Kameshwara, Kalyan Kumar, 2024. "Religious proximity and misinformation: Experimental evidence from a mobile phone-based campaign in India," Journal of Health Economics, Elsevier, vol. 96(C).
    42. Chen, Ya & Tsionas, Mike G. & Zelenyuk, Valentin, 2021. "LASSO+DEA for small and big wide data," Omega, Elsevier, vol. 102(C).
    43. Simone Maxand & Hend Sallam, 2024. "Local Fiscal Effects of Immigration in Germany," CESifo Working Paper Series 11162, CESifo.
    44. He, Yi & Jaidee, Sombut & Gao, Jiti, 2023. "Most powerful test against a sequence of high dimensional local alternatives," Journal of Econometrics, Elsevier, vol. 234(1), pages 151-177.
    45. D’Amour, Alexander & Ding, Peng & Feller, Avi & Lei, Lihua & Sekhon, Jasjeet, 2021. "Overlap in observational studies with high-dimensional covariates," Journal of Econometrics, Elsevier, vol. 221(2), pages 644-654.
    46. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
    47. Sylvain Barde & Rowan Cherodian & Guy Tchuente, 2023. "Moran's I Lasso for models with spatially correlated data," Papers 2310.02773, arXiv.org.
    48. Ahsan Jansson, Cecilia & Patil, Vikram & Vecci, Joe & Chellattan Veettil , Prakashan & Yashodha, Yashodha, 2023. "Locus of Control and Economic Decision-Making: A Field Experiment in Odisha, India," Working Papers in Economics 833, University of Gothenburg, Department of Economics.
    49. Joseph Antonelli & Matthew Cefalu & Nathan Palmer & Denis Agniel, 2018. "Doubly robust matching estimators for high dimensional confounding adjustment," Biometrics, The International Biometric Society, vol. 74(4), pages 1171-1179, December.
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  110. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers 62/13, Institute for Fiscal Studies.

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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  111. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Some New Asymptotic Theory for Least Squares Series: Pointwise and Uniform Results," Papers 1212.0442, arXiv.org, revised Jun 2015.

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    1. Dong, Chaohua & Linton, Oliver, 2018. "Additive nonparametric models with time variable and both stationary and nonstationary regressors," Journal of Econometrics, Elsevier, vol. 207(1), pages 212-236.
    2. Christoph Breunig, 2018. "Varying Random Coefficient Models," Papers 1804.03110, arXiv.org, revised Aug 2020.
    3. Michael Jansson & Demian Pouzo, 2019. "Towards a general large sample theory for regularized estimators," CeMMAP working papers CWP63/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Zheng Fang & Juwon Seo, 2021. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Econometrica, Econometric Society, vol. 89(5), pages 2439-2458, September.
    5. Drew Fudenberg & Whitney K. Newey & Philipp Strack & Tomasz Strzalecki, 2019. "Testing the Drift-Diffusion Model," Papers 1908.05824, arXiv.org.
    6. Babii, Andrii, 2020. "Honest Confidence Sets In Nonparametric Iv Regression And Other Ill-Posed Models," Econometric Theory, Cambridge University Press, vol. 36(4), pages 658-706, August.
    7. Cai, Zongwu & Juhl, Ted, 2023. "The distribution of rolling regression estimators," Journal of Econometrics, Elsevier, vol. 235(2), pages 1447-1463.
    8. Tadao Hoshino, 2024. "Functional Spatial Autoregressive Models," Papers 2402.14763, arXiv.org, revised Oct 2024.
    9. Ignacio Martinez & Jaume Vives-i-Bastida, 2022. "Bayesian and Frequentist Inference for Synthetic Controls," Papers 2206.01779, arXiv.org, revised Jul 2024.
    10. Hoshino, Tadao & Yanagi, Takahide, 2023. "Treatment effect models with strategic interaction in treatment decisions," Journal of Econometrics, Elsevier, vol. 236(2).
    11. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org, revised Nov 2024.
    12. Byunghoon Kang, 2017. "Inference in Nonparametric Series Estimation with Data-Dependent Undersmoothing," Working Papers 170712442, Lancaster University Management School, Economics Department.
    13. Tao, Jing, 2020. "Trinity tests of functions for conditional moment models," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
    14. Man, Rebeka & Tan, Kean Ming & Wang, Zian & Zhou, Wen-Xin, 2024. "Retire: Robust expectile regression in high dimensions," Journal of Econometrics, Elsevier, vol. 239(2).
    15. Marina Bassi & Lelys Dinarte-Diaz & Maria Marta Ferreyra & Sergio Urzua, 2023. "What Makes a Program Good? Evidence from Short-Cycle Higher Education Programs in Five Developing Countries," CESifo Working Paper Series 10255, CESifo.
    16. Chaohua Dong & Jiti Gao & Oliver Linton, 2018. "High dimensional semiparametric moment restriction models," CeMMAP working papers CWP04/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    17. Breunig, Christoph & Hoderlein, Stefan, 2018. "Specification Testing in Random Coefficient Models," Rationality and Competition Discussion Paper Series 77, CRC TRR 190 Rationality and Competition.
    18. Lin, Yingqian & Tu, Yundong, 2020. "Sieve extremum estimation of a semiparametric transformation model," Economics Letters, Elsevier, vol. 189(C).
    19. Giovanni Ballarin & Petros Dellaportas & Lyudmila Grigoryeva & Marcel Hirt & Sophie van Huellen & Juan-Pablo Ortega, 2022. "Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data," Papers 2211.00363, arXiv.org, revised Jan 2024.
    20. Cattaneo, Matias D & Jansson, Michael & Ma, Xinwei, 2021. "Local regression distribution estimators," Department of Economics, Working Paper Series qt7416d3x8, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    21. Hidehiko Ichimura & Whitney K. Newey, 2017. "The influence function of semiparametric estimators," CeMMAP working papers 06/17, Institute for Fiscal Studies.
    22. Byunghoon Kang, 2018. "Inference in Nonparametric Series Estimation with Specification Searches for the Number of Series Terms," Working Papers 240829404, Lancaster University Management School, Economics Department.
    23. Li, Jia & Liao, Zhipeng, 2020. "Uniform nonparametric inference for time series," Journal of Econometrics, Elsevier, vol. 219(1), pages 38-51.
    24. Matias D. Cattaneo & Richard K. Crump & Max H. Farrell & Yingjie Feng, 2019. "On Binscatter," Papers 1902.09608, arXiv.org, revised Apr 2024.
    25. Cattaneo, Matias D & Jansson, Michael & Newey, Whitney K, 2018. "Inference in Linear Regression Models with Many Covariates and Heteroscedasticity," Department of Economics, Working Paper Series qt6rp7p9gs, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    26. Holland, Ashley D., 2017. "Penalized spline estimation in the partially linear model," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 211-235.
    27. Victor Chernozhukov & Whitney K. Newey & Andres Santos, 2015. "Constrained conditional moment restriction models," CeMMAP working papers 59/15, Institute for Fiscal Studies.
    28. Zhao, Weihua & Lian, Heng, 2016. "Local asymptotics for nonparametric quantile regression with regression splines," Statistics & Probability Letters, Elsevier, vol. 117(C), pages 209-215.
    29. Lin, Yingqian & Tu, Yundong, 2024. "Functional coefficient cointegration models with Box–Cox transformation," Economics Letters, Elsevier, vol. 234(C).
    30. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2018. "Nonparametric estimation in case of endogenous selection," Journal of Econometrics, Elsevier, vol. 202(2), pages 268-285.
    31. Phillip Heiler, 2022. "Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization," Papers 2209.04329, arXiv.org, revised Jul 2024.
    32. Buchinsky, Moshe & Li, Fanghua & Liao, Zhipeng, 2022. "Estimation and inference of semiparametric models using data from several sources," Journal of Econometrics, Elsevier, vol. 226(1), pages 80-103.
    33. Liang Jiang & Liyao Li & Ke Miao & Yichong Zhang, 2023. "Adjustment with Many Regressors Under Covariate-Adaptive Randomizations," Papers 2304.08184, arXiv.org, revised Nov 2024.
    34. Chaohua Dong & Jiti Gao & Bin Peng, 2018. "Series estimation for single-index models under constraints," Monash Econometrics and Business Statistics Working Papers 5/18, Monash University, Department of Econometrics and Business Statistics.
    35. S. Centorrino & J. S. Racine, 2016. "Semiparametric Varying Coefficient Models with Endogenous Covariates," Department of Economics Working Papers 2016-02, McMaster University.
    36. Dong, Chaohua & Gao, Jiti & Linton, Oliver, 2023. "High dimensional semiparametric moment restriction models," Journal of Econometrics, Elsevier, vol. 232(2), pages 320-345.
    37. Parente, Paulo M.D.C. & Smith, Richard J., 2017. "Tests of additional conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 200(1), pages 1-16.
    38. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Ivan Fernandez-Val, 2016. "Conditional quantile processes based on series or many regressors," CeMMAP working papers 46/16, Institute for Fiscal Studies.
    39. Breunig, Christoph & Haan, Peter, 2021. "Nonparametric regression with selectively missing covariates," Journal of Econometrics, Elsevier, vol. 223(1), pages 28-52.
    40. Shi, Chengchun & Luo, Shikai & Zhu, Hongtu & Song, Rui, 2021. "An online sequential test for qualitative treatment effects," LSE Research Online Documents on Economics 112521, London School of Economics and Political Science, LSE Library.
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    Cited by:

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    2. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Testing many moment inequalities," CeMMAP working papers CWP42/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    4. Thilo Reinschlussel & Martin C. Arnold, 2024. "Information-Enriched Selection of Stationary and Non-Stationary Autoregressions using the Adaptive Lasso," Papers 2402.16580, arXiv.org, revised Jul 2024.
    5. Iván Díaz & Nima S. Hejazi, 2020. "Causal mediation analysis for stochastic interventions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 661-683, July.
    6. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2014. "Central limit theorems and bootstrap in high dimensions," CeMMAP working papers 49/14, Institute for Fiscal Studies.
    7. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, 2016. "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
    8. Hidalgo, Javier & Schafgans, Marcia, 2017. "Inference and testing breaks in large dynamic panels with strong cross sectional dependence," Journal of Econometrics, Elsevier, vol. 196(2), pages 259-274.
    9. Magne Mogstad & Joseph P. Romano & Daniel Wilhelm & Azeem M. Shaikh, 2020. "Inference for ranks with applications to mobility across neighborhoods and academic achievement across countries," CeMMAP working papers CWP10/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Peng Chen & Ivan Nourdin & Lihu Xu & Xiaochuan Yang & Rui Zhang, 2022. "Non-integrable Stable Approximation by Stein’s Method," Journal of Theoretical Probability, Springer, vol. 35(2), pages 1137-1186, June.
    11. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2016. "hdm: High-Dimensional Metrics," CeMMAP working papers 37/16, Institute for Fiscal Studies.
    12. Vira Semenova & Matt Goldman & Victor Chernozhukov & Matt Taddy, 2023. "Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 471-510, May.
    13. Xingcai Zhou & Zhaoyang Jing & Chao Huang, 2024. "Distributed Bootstrap Simultaneous Inference for High-Dimensional Quantile Regression," Mathematics, MDPI, vol. 12(5), pages 1-53, February.
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    38. Sven Klaassen & Jannis Kuck & Martin Spindler & Victor Chernozhukov, 2018. "Uniform Inference in High-Dimensional Gaussian Graphical Models," Papers 1808.10532, arXiv.org, revised Dec 2018.
    39. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2016. "Post-Selection Inference for Generalized Linear Models With Many Controls," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 606-619, October.
    40. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," Papers 1501.03185, arXiv.org.
    41. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2016. "Valid post-selection and post-regularization inference: An elementary, general approach," CeMMAP working papers CWP36/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    42. Jinyuan Chang & Wen Zhou & Wen-Xin Zhou & Lan Wang, 2017. "Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering," Biometrics, The International Biometric Society, vol. 73(1), pages 31-41, March.
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    44. Stefan Seifert & Marica Valente, 2018. "An Offer that you Can't Refuse? Agrimafias and Migrant Labor on Vineyards in Southern Italy," Discussion Papers of DIW Berlin 1735, DIW Berlin, German Institute for Economic Research.
    45. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2014. "Uniform post selection inference for LAD regression and other Z-estimation problems," CeMMAP working papers 51/14, Institute for Fiscal Studies.
    46. Victor Chernozhukov & Whitney Newey & Rahul Singh, 2018. "De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers," Papers 1802.08667, arXiv.org, revised Oct 2022.
    47. Xu, Kai & Cheng, Qing, 2024. "Test of conditional independence in factor models via Hilbert–Schmidt independence criterion," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    48. Jelena Bradic & Victor Chernozhukov & Whitney K. Newey & Yinchu Zhu, 2019. "Minimax Semiparametric Learning With Approximate Sparsity," Papers 1912.12213, arXiv.org, revised Aug 2022.
    49. Matias D. Cattaneo & Yingjie Feng & Rocio Titiunik, 2019. "Prediction Intervals for Synthetic Control Methods," Papers 1912.07120, arXiv.org, revised Sep 2021.
    50. Olmo, José, 2016. "Long-term optimal portfolio allocation under dynamic horizon-specific risk aversion," UC3M Working papers. Economics 23599, Universidad Carlos III de Madrid. Departamento de Economía.
    51. Denis Chetverikov & Bradley Larsen & Christopher Palmer, 2016. "IV Quantile Regression for Group‐Level Treatments, With an Application to the Distributional Effects of Trade," Econometrica, Econometric Society, vol. 84, pages 809-833, March.
    52. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings," CeMMAP working papers CWP38/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    53. Chetverikov, Denis & Wilhelm, Daniel & Kim, Dongwoo, 2021. "An Adaptive Test Of Stochastic Monotonicity," Econometric Theory, Cambridge University Press, vol. 37(3), pages 495-536, June.
    54. Harold D. Chiang, 2019. "Many Average Partial Effects: with an Application to Text Regression," 2019 Papers pch1836, Job Market Papers.
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    56. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2019. "Improved Central Limit Theorem and bootstrap approximations in high dimensions," Papers 1912.10529, arXiv.org, revised May 2022.
    57. Shi, Chengchun & Li, Lexin, 2022. "Testing mediation effects using logic of Boolean matrices," LSE Research Online Documents on Economics 108881, London School of Economics and Political Science, LSE Library.
    58. Robert Adamek & Stephan Smeekes & Ines Wilms, 2020. "Lasso Inference for High-Dimensional Time Series," Papers 2007.10952, arXiv.org, revised Sep 2022.
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    60. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers 47/16, Institute for Fiscal Studies.
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    79. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul, 2017. "Confidence bands for coefficients in high dimensional linear models with error-in-variables," CeMMAP working papers 22/17, Institute for Fiscal Studies.
    80. Robert Adamek & Stephan Smeekes & Ines Wilms, 2023. "Sparse High-Dimensional Vector Autoregressive Bootstrap," Papers 2302.01233, arXiv.org.
    81. Cheng, Guanghui & Liu, Zhi & Peng, Liuhua, 2022. "Gaussian approximations for high-dimensional non-degenerate U-statistics via exchangeable pairs," Statistics & Probability Letters, Elsevier, vol. 182(C).
    82. Philipp Bach & Sven Klaassen & Jannis Kueck & Martin Spindler, 2020. "Estimation and Uniform Inference in Sparse High-Dimensional Additive Models," Papers 2004.01623, arXiv.org, revised Apr 2024.
    83. Wenlong Ji & Lihua Lei & Asher Spector, 2023. "Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects," Papers 2310.08115, arXiv.org, revised Nov 2024.
    84. Matteo Barigozzi, 2022. "On Estimation and Inference of Large Approximate Dynamic Factor Models via the Principal Component Analysis," Papers 2211.01921, arXiv.org, revised Jul 2023.
    85. Yuta Koike, 2023. "High-Dimensional Central Limit Theorems for Homogeneous Sums," Journal of Theoretical Probability, Springer, vol. 36(1), pages 1-45, March.
    86. Timothy B. Armstrong & Michal Kolesár & Soonwoo Kwon, 2020. "Bias-Aware Inference in Regularized Regression Models," Working Papers 2020-2, Princeton University. Economics Department..
    87. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers CWP47/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    88. Jana Janková & Rajen D. Shah & Peter Bühlmann & Richard J. Samworth, 2020. "Goodness‐of‐fit testing in high dimensional generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 773-795, July.
    89. Martin C. Arnold & Thilo Reinschlussel, 2024. "Bootstrap Adaptive Lasso Solution Path Unit Root Tests," Papers 2409.07859, arXiv.org.
    90. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    91. Naumov, A. & Spokoiny, V. & Ulyanovk, V., 2018. "Bootstrap Confidence Sets for Spectral Projectors of Sample Covariance," IRTG 1792 Discussion Papers 2018-024, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    92. Kojevnikov, Denis & Song, Kyungchul, 2022. "A Berry–Esseen bound for vector-valued martingales," Statistics & Probability Letters, Elsevier, vol. 186(C).
    93. Zhengbang Li & Fuxiang Liu & Luanjie Zeng & Guoxin Zuo, 2021. "A stationary bootstrap test about two mean vectors comparison with somewhat dense differences and fewer sample size than dimension," Computational Statistics, Springer, vol. 36(2), pages 941-960, June.
    94. Lixiu Wu & Jiang Hu, 2024. "Multi-sample hypothesis testing of high-dimensional mean vectors under covariance heterogeneity," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(4), pages 579-615, August.
    95. Wu Wang & Xuming He & Zhongyi Zhu, 2020. "Statistical inference for multiple change‐point models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1149-1170, December.
    96. Philipp Bach & Victor Chernozhukov & Martin Spindler, 2019. "Valid simultaneous inference in high-dimensional settings (with the HDM package for R)," CeMMAP working papers CWP30/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    97. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    98. Ouyang, Yanyan & Liu, Jiamin & Tong, Tiejun & Xu, Wangli, 2022. "A rank-based high-dimensional test for equality of mean vectors," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    99. Joel L. Horowitz, 2018. "Bootstrap Methods in Econometrics," Papers 1809.04016, arXiv.org.
    100. Victor Chernozhukov & Chris Hansen & Martin Spindler, 2016. "High-Dimensional Metrics in R," Papers 1603.01700, arXiv.org, revised Aug 2016.
    101. Hidalgo, Javier & Schafgans, Marcia, 2017. "Inference and testing breaks in large dynamic panels with strong cross sectional dependence," LSE Research Online Documents on Economics 68839, London School of Economics and Political Science, LSE Library.
    102. Rahul Singh & Liyang Sun, 2019. "Double Robustness for Complier Parameters and a Semiparametric Test for Complier Characteristics," Papers 1909.05244, arXiv.org, revised Dec 2022.
    103. Alexander Giessing & Jianqing Fan, 2020. "Bootstrapping $\ell_p$-Statistics in High Dimensions," Papers 2006.13099, arXiv.org, revised Aug 2020.
    104. Victor Chernozhukov & Whitney K Newey & Rahul Singh, 2018. "Automatic Debiased Machine Learning of Causal and Structural Effects," Papers 1809.05224, arXiv.org, revised Oct 2022.
    105. Spokoiny, Vladimir & Zhilova, Mayya, 2014. "Bootstrap confidence sets under model misspecification," SFB 649 Discussion Papers 2014-067, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    106. Alexandre Belloni & Mathieu Rosenbaum & Alexandre B. Tsybakov, 2017. "Linear and conic programming estimators in high dimensional errors-in-variables models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 939-956, June.
    107. Demian Pouzo, 2014. "Bootstrap Consistency for Quadratic Forms of Sample Averages with Increasing Dimension," Papers 1411.2701, arXiv.org, revised Aug 2015.
    108. Victor Chernozhukov & Whitney K. Newey & James Robins, 2018. "Double/de-biased machine learning using regularized Riesz representers," CeMMAP working papers CWP15/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    109. Keith Battocchi & Eleanor Dillon & Maggie Hei & Greg Lewis & Miruna Oprescu & Vasilis Syrgkanis, 2021. "Estimating the Long-Term Effects of Novel Treatments," Papers 2103.08390, arXiv.org, revised Feb 2022.
    110. Yang, Weichao & Guo, Xu & Zhu, Lixing, 2024. "Tests for high-dimensional generalized linear models under general covariance structure," Computational Statistics & Data Analysis, Elsevier, vol. 199(C).
    111. Denis Chetverikov & Jesper Riis-Vestergaard S{o}rensen, 2021. "Selecting Penalty Parameters of High-Dimensional M-Estimators using Bootstrapping after Cross-Validation," Papers 2104.04716, arXiv.org, revised Nov 2024.
    112. Mengjia Yu & Xiaohui Chen, 2021. "Finite sample change point inference and identification for high‐dimensional mean vectors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(2), pages 247-270, April.
    113. Joel L. Horowitz, 2017. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers 46/17, Institute for Fiscal Studies.
    114. Victor Chernozhukov & Christian Hansen & Yuan Liao & Yinchu Zhu, 2021. "Inference for Low-Rank Models," Papers 2107.02602, arXiv.org, revised Jan 2023.
    115. Joel L. Horowitz, 2018. "Non-Asymptotic Inference in Instrumental Variables Estimation," Papers 1809.03600, arXiv.org.
    116. Belloni, Alexandre & Hansen, Christian & Newey, Whitney, 2022. "High-dimensional linear models with many endogenous variables," Journal of Econometrics, Elsevier, vol. 228(1), pages 4-26.
    117. Horowitz, Joel L., 2021. "Bounding the difference between true and nominal rejection probabilities in tests of hypotheses about instrumental variables models," Journal of Econometrics, Elsevier, vol. 222(2), pages 1057-1082.
    118. Peter Horvath & Jia Li & Zhipeng Liao & Andrew J. Patton, 2022. "A consistent specification test for dynamic quantile models," Quantitative Economics, Econometric Society, vol. 13(1), pages 125-151, January.
    119. Wei, Waverly & Zhou, Yuqing & Zheng, Zeyu & Wang, Jingshen, 2024. "Inference on the best policies with many covariates," Journal of Econometrics, Elsevier, vol. 239(2).
    120. Chang, Jinyuan & Hu, Qiao & Kolaczyk, Eric D. & Yao, Qiwei & Yi, Fengting, 2024. "Edge differentially private estimation in the β-model via jittering and method of moments," LSE Research Online Documents on Economics 122099, London School of Economics and Political Science, LSE Library.
    121. Kean Ming Tan & Junwei Lu & Tong Zhang & Han Liu, 2021. "Estimating and inferring the maximum degree of stimulus‐locked time‐varying brain connectivity networks," Biometrics, The International Biometric Society, vol. 77(2), pages 379-390, June.
    122. Ye Luo & Hai Wang, 2017. "Core Determining Class and Inequality Selection," American Economic Review, American Economic Association, vol. 107(5), pages 274-277, May.
    123. Thomas M. Russell, 2020. "Policy Transforms and Learning Optimal Policies," Papers 2012.11046, arXiv.org.
    124. Joel L. Horowitz, 2018. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers CWP52/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    125. Victor Chernozhukov & Ivan Fernandez-Val & Siyi Luo, 2023. "Distribution regression with sample selection and UK wage decomposition," CeMMAP working papers 09/23, Institute for Fiscal Studies.
    126. Cyrus Samii & Ye Wang & Jonathan Sullivan & P. M. Aronow, 2023. "Inference in Spatial Experiments with Interference using the SpatialEffect Package," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(1), pages 138-156, March.
    127. Chang, Jinyuan & Yao, Qiwei & Zhou, Wen, 2017. "Testing for high-dimensional white noise using maximum cross-correlations," LSE Research Online Documents on Economics 68531, London School of Economics and Political Science, LSE Library.
    128. David M. Ritzwoller & Vasilis Syrgkanis, 2024. "Simultaneous Inference for Local Structural Parameters with Random Forests," Papers 2405.07860, arXiv.org, revised Sep 2024.
    129. Zhilova, Mayya, 2015. "Simultaneous likelihood-based bootstrap confidence sets for a large number of models," SFB 649 Discussion Papers 2015-031, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    130. Joel L. Horowitz, 2018. "Bootstrap methods in econometrics," CeMMAP working papers CWP53/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    131. Baek, Changryong & Gates, Katheleen M. & Leinwand, Benjamin & Pipiras, Vladas, 2021. "Two sample tests for high-dimensional autocovariances," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).

  113. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Central limit theorems and multiplier bootstrap when p is much larger than n," CeMMAP working papers 45/12, Institute for Fiscal Studies.

    Cited by:

    1. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Comparison and anti-concentration bounds for maxima of Gaussian random vectors," CeMMAP working papers 71/13, Institute for Fiscal Studies.

  114. Victor Chernozhukov & Emre Kocatulum & Konrad Menzel, 2012. "Inference on Sets in Finance," Papers 1211.4282, arXiv.org.

    Cited by:

    1. Hiroaki Kaido, 2014. "Asymptotically efficient estimation of weighted average derivatives with an interval censored variable," CeMMAP working papers 03/14, Institute for Fiscal Studies.
    2. Yuan Liao & Anna Simoni, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," Papers 1212.3267, arXiv.org, revised Nov 2013.
    3. Otsu, Taisuke & Matsushita, Yukitoshi & Xu, Ke-Li, 2014. "Empirical likelihood for regression discontinuity design," LSE Research Online Documents on Economics 58065, London School of Economics and Political Science, LSE Library.
    4. Bontemps, Christian & Kumar, Rohit, 2018. "A Geometric Approach to Inference in Set-Identified Entry Games," TSE Working Papers 18-943, Toulouse School of Economics (TSE), revised Mar 2019.
    5. Yuan Liao & Anna Simoni, 2019. "Bayesian inference for partially identified smooth convex models," Post-Print hal-03089881, HAL.
    6. Gospodinov, Nikolay & Kan, Raymond & Robotti, Cesare, 2016. "On the properties of the constrained Hansen–Jagannathan distance," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 121-150.
    7. Karun Adusumilli & Taisuke Otsu, 2014. "Empirical Likelihood for Random Sets," STICERD - Econometrics Paper Series 574, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    8. Hiroaki Kaido & Jiaxuan Li & Marc Rysman, 2018. "Moment Inequalities in the Context of Simulated and Predicted Variables," Papers 1804.03674, arXiv.org.
    9. Yuan Liao & Anna Simoni, 2016. "Bayesian Inference for Partially Identified Convex Models: Is it Valid for Frequentist Inference?," Departmental Working Papers 201607, Rutgers University, Department of Economics.

  115. Arun Chandrasekhar & Victor Chernozhukov & Francesca Molinari & Paul Schrimpf, 2012. "Inference for best linear approximations to set identified functions," CeMMAP working papers 43/12, Institute for Fiscal Studies.

    Cited by:

    1. Hiroaki Kaido, 2014. "Asymptotically efficient estimation of weighted average derivatives with an interval censored variable," CeMMAP working papers 03/14, Institute for Fiscal Studies.
    2. Yuan Liao & Anna Simoni, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," Papers 1212.3267, arXiv.org, revised Nov 2013.
    3. Vira Semenova, 2023. "Aggregated Intersection Bounds and Aggregated Minimax Values," Papers 2303.00982, arXiv.org, revised Jun 2024.
    4. Vira Semenova, 2017. "Debiased Machine Learning of Set-Identified Linear Models," Papers 1712.10024, arXiv.org, revised Dec 2022.
    5. Otsu, Taisuke & Matsushita, Yukitoshi & Xu, Ke-Li, 2014. "Empirical likelihood for regression discontinuity design," LSE Research Online Documents on Economics 58065, London School of Economics and Political Science, LSE Library.
    6. Ben Sand & Chris Bidner, 2016. "Job Prospects and Pay Gaps: Theory and Evidence on the Gender Gap from U.S. Cities," Discussion Papers dp16-14, Department of Economics, Simon Fraser University.
    7. Yuan Liao & Anna Simoni, 2019. "Bayesian inference for partially identified smooth convex models," Post-Print hal-03089881, HAL.
    8. Juan Carlos Escanciano & Lin Zhu, 2013. "Set inferences and sensitivity analysis in semiparametric conditionally identified models," CeMMAP working papers 55/13, Institute for Fiscal Studies.
    9. Lee, Y-Y. & Bhattacharya, D., 2018. "Applied Welfare Analysis for Discrete Choice with Interval-data on Income," Cambridge Working Papers in Economics 1882, Faculty of Economics, University of Cambridge.
    10. Vira Semenova, 2020. "Generalized Lee Bounds," Papers 2008.12720, arXiv.org, revised Feb 2023.
    11. Francesca Molinari, 2020. "Microeconometrics with Partial Identi?cation," CeMMAP working papers CWP15/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Karun Adusumilli & Taisuke Otsu, 2014. "Empirical Likelihood for Random Sets," STICERD - Econometrics Paper Series 574, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    13. Christian Bontemps & Thierry Magnac, 2017. "Set Identification, Moment Restrictions, and Inference," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 103-129, September.
    14. Wenlong Ji & Lihua Lei & Asher Spector, 2023. "Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects," Papers 2310.08115, arXiv.org, revised Nov 2024.
    15. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
    17. Hoshino, Tadao, 2013. "Partial identification in binary response models with nonignorable nonresponses," Economics Letters, Elsevier, vol. 121(1), pages 74-78.
    18. Maasoumi, Esfandiar & Wang, Le, 2017. "What can we learn about the racial gap in the presence of sample selection?," Journal of Econometrics, Elsevier, vol. 199(2), pages 117-130.
    19. Yuan Liao & Anna Simoni, 2016. "Bayesian Inference for Partially Identified Convex Models: Is it Valid for Frequentist Inference?," Departmental Working Papers 201607, Rutgers University, Department of Economics.

  116. Alexandre Belloni & Victor Chernozhukov, 2011. "High Dimensional Sparse Econometric Models: An Introduction," Papers 1106.5242, arXiv.org, revised Sep 2011.

    Cited by:

    1. Caner, Mehmet & Kock, Anders Bredahl, 2018. "Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso," Journal of Econometrics, Elsevier, vol. 203(1), pages 143-168.
    2. Áureo de Paula & Imran Rasul & Pedro CL Souza, 2023. "Identifying network ties from panel data: Theory and an application to tax competition," CeMMAP working papers 21/23, Institute for Fiscal Studies.
    3. Liqian Cai & Arnab Bhattacharjee & Roger Calantone & Taps Maiti, 2019. "Variable Selection with Spatially Autoregressive Errors: A Generalized Moments LASSO Estimator," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 146-200, September.
    4. Bottmer, Lea & Croux, Christophe & Wilms, Ines, 2022. "Sparse regression for large data sets with outliers," European Journal of Operational Research, Elsevier, vol. 297(2), pages 782-794.
    5. Alquier, Pierre & Hebiri, Mohamed, 2011. "Generalization of ℓ1 constraints for high dimensional regression problems," Statistics & Probability Letters, Elsevier, vol. 81(12), pages 1760-1765.
    6. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2024. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 605-635.
    7. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers 62/13, Institute for Fiscal Studies.
    8. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2019. "lassopack: Model selection and prediction with regularized regression in Stata," Papers 1901.05397, arXiv.org.
    9. Laurent Callot & Johannes Tang Kristensen, 2014. "Vector Autoregressions with parsimoniously Time Varying Parameters and an Application to Monetary Policy," Tinbergen Institute Discussion Papers 14-145/III, Tinbergen Institute, revised 09 Apr 2015.
    10. Federico A. Bugni & Mehmet Caner & Anders Bredahl Kock & Soumendra Lahiri, 2016. "Inference in partially identified models with many moment inequalities using Lasso," CREATES Research Papers 2016-12, Department of Economics and Business Economics, Aarhus University.
    11. Mehmet Caner & Anders Bredahl Kock, 2013. "Oracle Inequalities for Convex Loss Functions with Non-Linear Targets," CREATES Research Papers 2013-51, Department of Economics and Business Economics, Aarhus University.
    12. Anders Bredahl Kock & Laurent A.F. Callot, 2012. "Oracle Inequalities for High Dimensional Vector Autoregressions," CREATES Research Papers 2012-16, Department of Economics and Business Economics, Aarhus University.
    13. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Sparse Graphical Vector Autoregression: A Bayesian Approach," Annals of Economics and Statistics, GENES, issue 123-124, pages 333-361.
    14. Malene Kallestrup-Lamb & Anders Bredahl Kock & Johannes Tang Kristensen, 2016. "Lassoing the Determinants of Retirement," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1522-1561, December.
    15. André Nunes Maranhão, 2024. "Brazilian Business Cycle Analysis in a High-Dimensional and Time-Irregular Span Context," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 20(1), pages 1-58, August.
    16. Simon Freyaldenhoven, 2020. "Identification Through Sparsity in Factor Models," Working Papers 20-25, Federal Reserve Bank of Philadelphia.
    17. André Nunes Maranhão & Nicole Rennó Castro, 2023. "Dissecting Brazilian agriculture business cycles in high-dimensional and time-irregular span contexts," Empirical Economics, Springer, vol. 65(4), pages 1543-1578, October.
    18. Jorge Balat & Camila Casas, 2018. "Firm Productivity and Cities: The Case of Colombia," Borradores de Economia 1032, Banco de la Republica de Colombia.
    19. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers 47/16, Institute for Fiscal Studies.
    20. Ulrike Schneider, 2016. "Confidence Sets Based on Thresholding Estimators in High-Dimensional Gaussian Regression Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1412-1455, December.
    21. Xu, Ning & Hong, Jian & Fisher, Timothy, 2016. "Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso," MPRA Paper 71670, University Library of Munich, Germany.
    22. Yamada, Hiroshi & Yoon, Gawon, 2014. "When Grilli and Yang meet Prebisch and Singer: Piecewise linear trends in primary commodity prices," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 193-207.
    23. Achim Ahrens & Arnab Bhattacharjee, 2015. "Two-Step Lasso Estimation of the Spatial Weights Matrix," Econometrics, MDPI, vol. 3(1), pages 1-28, March.
    24. Newhouse,David Locke & Merfeld,Joshua David & Ramakrishnan,Anusha Pudugramam & Swartz,Tom & Lahiri,Partha, 2022. "Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning," Policy Research Working Paper Series 10175, The World Bank.
    25. Awijen, Haithem & Ben Zaied, Younes & Ben Lahouel, Béchir & Khlifi, Foued, 2023. "Machine learning for US cross-industry return predictability under information uncertainty," Research in International Business and Finance, Elsevier, vol. 64(C).
    26. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers CWP47/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    27. Kock, Anders Bredahl, 2016. "Oracle inequalities, variable selection and uniform inference in high-dimensional correlated random effects panel data models," Journal of Econometrics, Elsevier, vol. 195(1), pages 71-85.
    28. Masaki,Takaaki & Newhouse,David Locke & Silwal,Ani Rudra & Bedada,Adane & Engstrom,Ryan, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," Policy Research Working Paper Series 9383, The World Bank.
    29. Michael Zimmert, 2018. "The Finite Sample Performance of Treatment Effects Estimators based on the Lasso," Papers 1805.05067, arXiv.org.
    30. Alberto Abadie & Maximilian Kasy, 2019. "Choosing Among Regularized Estimators in Empirical Economics: The Risk of Machine Learning," The Review of Economics and Statistics, MIT Press, vol. 101(5), pages 743-762, December.
    31. De La Maza, Cristóbal & Davis, Alex & Azevedo, Inês, 2021. "Welfare analysis of the ecological impacts of electricity production in Chile using the sparse multinomial logit model," Ecological Economics, Elsevier, vol. 184(C).
    32. Kalouptsidi, Myrto, 2017. "Detection and Impact of Industrial Subsidies: The Case of Chinese Shipbuilding," CEPR Discussion Papers 12080, C.E.P.R. Discussion Papers.
    33. Damian Kozbur, 2013. "Inference in additively separable models with a high-dimensional set of conditioning variables," ECON - Working Papers 284, Department of Economics - University of Zurich, revised Apr 2018.
    34. Ng, Serena, 2013. "Variable Selection in Predictive Regressions," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 752-789, Elsevier.
    35. Riccardo Di Francesco, 2023. "Ordered Correlation Forest," Papers 2309.08755, arXiv.org.
    36. Zhu, Ying, 2018. "Sparse linear models and l1-regularized 2SLS with high-dimensional endogenous regressors and instruments," Journal of Econometrics, Elsevier, vol. 202(2), pages 196-213.
    37. Myrto Kalouptsidi, 2014. "Detection and Impact of Industrial Subsidies: The Case of World Shipbuilding," NBER Working Papers 20119, National Bureau of Economic Research, Inc.
    38. Michael P. Leung & Pantelis Loupos, 2022. "Graph Neural Networks for Causal Inference Under Network Confounding," Papers 2211.07823, arXiv.org, revised Mar 2024.
    39. Dai, Wei & Tsang, Ka Wai, 2023. "A resampling approach for confidence intervals in linear time-series models after model selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).

  117. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls," Papers 1201.0224, arXiv.org, revised May 2012.

    Cited by:

    1. Aline Bütikofer & Rita Ginja & Fanny Landaud & Katrine Løken, 2020. "School Selectivity, Peers, and Mental Health," Working Papers 2020-074, Human Capital and Economic Opportunity Working Group.
    2. Caner, Mehmet & Kock, Anders Bredahl, 2018. "Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso," Journal of Econometrics, Elsevier, vol. 203(1), pages 143-168.
    3. Rao, Manaswini & Shenoy, Ashish, 2023. "Got (clean) milk? Organization, incentives, and management in Indian dairy cooperatives," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 708-722.
    4. Toru Kitagawa & Chris Muris, 2015. "Model averaging in semiparametric estimation of treatment effects," CeMMAP working papers CWP46/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Michael J. Weir & Thomas W. Sproul, 2019. "Identifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment," Sustainability, MDPI, vol. 11(14), pages 1-21, July.
    6. Imbens, Guido W., 2014. "Matching Methods in Practice: Three Examples," IZA Discussion Papers 8049, Institute of Labor Economics (IZA).
    7. Shukla, Pallavi & Pullabhotla, Hemant K. & Baylis, Kathy, 2022. "Trouble with zero: The limits of subsidizing technology adoption," Journal of Development Economics, Elsevier, vol. 158(C).
    8. Chakravorty, Bhaskar & Arulampalam, Wiji & Bhatiya, Apurav Yash & Imbert, Clement & Rathelot, Roland, 2021. "Can information about jobs improve the effectiveness of vocational training? Experimental evidence from India," CAGE Online Working Paper Series 567, Competitive Advantage in the Global Economy (CAGE).
    9. Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," IZA Discussion Papers 10961, Institute of Labor Economics (IZA).
    10. Alexandre Belloni & Victor Chernozhukov & Christian Hansen & Damian Kozbur, 2014. "Inference in High Dimensional Panel Models with an Application to Gun Control," Papers 1411.6507, arXiv.org.
    11. González, F & Muñoz, P & Prem, M, 2019. "Lost in Transition? The Persistence of Dictatorship Mayors," Documentos de Trabajo 17431, Universidad del Rosario.
    12. Yash Raj Shrestha & Vivianna Fang He & Phanish Puranam & Georg von Krogh, 2021. "Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize?," Organization Science, INFORMS, vol. 32(3), pages 856-880, May.
    13. Alexander Krei{ss} & Christoph Rothe, 2021. "Inference in Regression Discontinuity Designs with High-Dimensional Covariates," Papers 2110.13725, arXiv.org, revised May 2022.
    14. Teck-Hua Ho & Noah Lim & Sadat Reza & Xiaoyu Xia, 2017. "OM Forum—Causal Inference Models in Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 509-525, October.
    15. Wunsch, Conny & Strittmatter, Anthony, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," CEPR Discussion Papers 15840, C.E.P.R. Discussion Papers.
    16. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2016. "Post-Selection Inference for Generalized Linear Models With Many Controls," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 606-619, October.
    17. Clarke, Damian, 2023. "The Economics of Abortion Policy," IZA Discussion Papers 16395, Institute of Labor Economics (IZA).
    18. Angrist, Joshua & Rokkanen, Miikka, 2013. "Wanna Get Away? RD Identification Away from the Cutoff," IZA Discussion Papers 7429, Institute of Labor Economics (IZA).
    19. Alex Armand & Britta Augsburg & Antonella Bancalari, 2021. "Coordination and the poor maintenance trap: an experiment on public infrastructure in India," IFS Working Papers W21/16, Institute for Fiscal Studies.
    20. John List & Ian Muir & Gregory Sun, 2022. "Using Machine Learning for Efficient Flexible Regression Adjustment in Economic Experiments," Natural Field Experiments 00763, The Field Experiments Website.
    21. Stefano Cabras & J. D. Tena, 2023. "Implicit institutional incentives and individual decisions: Causal inference with deep learning models," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(6), pages 3739-3754, September.
    22. Alexander Kreiss & Christoph Rothe, 2023. "Inference in regression discontinuity designs with high-dimensional covariates," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 105-123.
    23. Kaila, Heidi & Azad, Abul, 2023. "The effects of crime and violence on food insecurity and consumption in Nigeria," Food Policy, Elsevier, vol. 115(C).
    24. Haroon, Maryiam & Said, Farah & Zafar, Mahniya, 2022. "Fostering non-cognitive skills and academic performance: Experimental evidence from women’s-only colleges in Pakistan," Journal of Asian Economics, Elsevier, vol. 81(C).
    25. Hensel, Lukas & Tekleselassie, Tsegay & Witte, Marc J., 2021. "Formalized Employee Search and Labor Demand," IZA Discussion Papers 14839, Institute of Labor Economics (IZA).
    26. Miric, Milan & Boudreau, Kevin J. & Jeppesen, Lars Bo, 2019. "Protecting their digital assets: The use of formal & informal appropriability strategies by App developers," Research Policy, Elsevier, vol. 48(8), pages 1-1.
    27. Ji, Chengyuan & Jiang, Junyan & Zhang, Yujin, 2024. "Political trust and government performance in the time of COVID-19," World Development, Elsevier, vol. 176(C).
    28. Liran Einav & Jonathan D. Levin, 2013. "The Data Revolution and Economic Analysis," NBER Working Papers 19035, National Bureau of Economic Research, Inc.
    29. Toru Kitagawa & Chris Muris, 2013. "Covariate selection and model averaging in semiparametric estimation of treatment effects," CeMMAP working papers 61/13, Institute for Fiscal Studies.
    30. Zhu, Ying, 2013. "Sparse Linear Models and Two-Stage Estimation in High-Dimensional Settings with Possibly Many Endogenous Regressors," MPRA Paper 49846, University Library of Munich, Germany.
    31. Jin, Wei & van der Ploeg, Frederick & Zhang, Lin, 2024. "How clean capital slows down disinvestment of carbon-intensive capital in the low-carbon transition," Journal of Economic Dynamics and Control, Elsevier, vol. 162(C).
    32. Helmut Wasserbacher & Martin Spindler, 2024. "Credit Ratings: Heterogeneous Effect on Capital Structure," Papers 2406.18936, arXiv.org.
    33. Kovacs, Roxanne J. & Lagarde, Mylene & Cairns, John, 2022. "Can patients improve the quality of care they receive? Experimental evidence from Senegal," World Development, Elsevier, vol. 150(C).
    34. Loh, Wen Wei & Ren, Dongning, 2021. "Data-driven Covariate Selection for Confounding Adjustment by Focusing on the Stability of the Effect Estimator," OSF Preprints yve6u, Center for Open Science.
    35. Matthias Breuer & Harm H. Schütt, 2023. "Accounting for uncertainty: an application of Bayesian methods to accruals models," Review of Accounting Studies, Springer, vol. 28(2), pages 726-768, June.
    36. Byol Kim & Song Liu & Mladen Kolar, 2021. "Two‐sample inference for high‐dimensional Markov networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 939-962, November.
    37. Chakravorty, Bhaskar & Bhatiya, Apurav Yash & Imbert, Clement & Lohnert, Maximilian & Panda, Poonam & Rathelot, Roland, 2022. "Impact of the COVID-19 Crisis on India’s Rural Youth : Evidence from a Panel Survey and an Experiment," The Warwick Economics Research Paper Series (TWERPS) 1419, University of Warwick, Department of Economics.
    38. Ya Chen & Mike Tsionas & Valentin Zelenyuk, 2020. "LASSO DEA for small and big data," CEPA Working Papers Series WP092020, School of Economics, University of Queensland, Australia.
    39. Travis J. Lybbert & Ashish Shenoy & Tomoé Bourdier & Caitlin Kieran, 2024. "Striving to revive pulses in India with extension, input subsidies, and output price supports†," American Journal of Agricultural Economics, John Wiley & Sons, vol. 106(3), pages 1167-1192, May.
    40. Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
    41. Alex Armand & Britta Augsburg & Antonella Bancalari & Kalyan Kumar Kameshwara, 2023. "Social proximity and misinformation: Experimental evidence from a mobile phone-based campaign in India," IFS Working Papers W23/39, Institute for Fiscal Studies.
    42. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers CWP59/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    43. Ian W. McKeague & Min Qian, 2015. "An Adaptive Resampling Test for Detecting the Presence of Significant Predictors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1422-1433, December.
    44. Martin C. Arnold & Thilo Reinschlussel, 2024. "Bootstrap Adaptive Lasso Solution Path Unit Root Tests," Papers 2409.07859, arXiv.org.
    45. Renée, Laëtitia, 2022. "The long-term effects of financial aid and career education: Evidence from a randomized experiment," CLEF Working Paper Series 46, Canadian Labour Economics Forum (CLEF), University of Waterloo.
    46. Armand, Alex & Augsburg, Britta & Bancalari, Antonella & Ghatak, Maitreesh, 2023. "Public Service Delivery, Exclusion and Externalities: Theory and Experimental Evidence from India," CEPR Discussion Papers 18636, C.E.P.R. Discussion Papers.
    47. Alexandre Belloni & Victor Chernozhukov, 2015. "Comment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1449-1451, December.
    48. Mckenzie,David J. & Sansone,Dario & Mckenzie,David J. & Sansone,Dario, 2017. "Man vs. machine in predicting successful entrepreneurs : evidence from a business plan competition in Nigeria," Policy Research Working Paper Series 8271, The World Bank.
    49. David Cheng & Abhishek Chakrabortty & Ashwin N. Ananthakrishnan & Tianxi Cai, 2020. "Estimating average treatment effects with a double‐index propensity score," Biometrics, The International Biometric Society, vol. 76(3), pages 767-777, September.
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    52. Armand, Alex & Augsburg, Britta & Bancalari, Antonella & Kameshwara, Kalyan Kumar, 2024. "Religious proximity and misinformation: Experimental evidence from a mobile phone-based campaign in India," Journal of Health Economics, Elsevier, vol. 96(C).
    53. Chen, Ya & Tsionas, Mike G. & Zelenyuk, Valentin, 2021. "LASSO+DEA for small and big wide data," Omega, Elsevier, vol. 102(C).
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    55. Simone Maxand & Hend Sallam, 2024. "Local Fiscal Effects of Immigration in Germany," CESifo Working Paper Series 11162, CESifo.
    56. He, Yi & Jaidee, Sombut & Gao, Jiti, 2023. "Most powerful test against a sequence of high dimensional local alternatives," Journal of Econometrics, Elsevier, vol. 234(1), pages 151-177.
    57. D’Amour, Alexander & Ding, Peng & Feller, Avi & Lei, Lihua & Sekhon, Jasjeet, 2021. "Overlap in observational studies with high-dimensional covariates," Journal of Econometrics, Elsevier, vol. 221(2), pages 644-654.
    58. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
    59. Rishabh Tyagi & Peter Eibich & Vegard Skirbekk, 2024. "Gender norms and partnership dissolution following involuntary job loss in Germany," MPIDR Working Papers WP-2024-027, Max Planck Institute for Demographic Research, Rostock, Germany.
    60. Sylvain Barde & Rowan Cherodian & Guy Tchuente, 2023. "Moran's I Lasso for models with spatially correlated data," Papers 2310.02773, arXiv.org.
    61. Ahsan Jansson, Cecilia & Patil, Vikram & Vecci, Joe & Chellattan Veettil , Prakashan & Yashodha, Yashodha, 2023. "Locus of Control and Economic Decision-Making: A Field Experiment in Odisha, India," Working Papers in Economics 833, University of Gothenburg, Department of Economics.
    62. Joseph Antonelli & Matthew Cefalu & Nathan Palmer & Denis Agniel, 2018. "Doubly robust matching estimators for high dimensional confounding adjustment," Biometrics, The International Biometric Society, vol. 74(4), pages 1171-1179, December.
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  118. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Iv'an Fern'andez-Val, 2011. "Conditional Quantile Processes based on Series or Many Regressors," Papers 1105.6154, arXiv.org, revised Aug 2018.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "On the asymptotic theory for least squares series: pointwise and uniform results," CeMMAP working papers 73/13, Institute for Fiscal Studies.
    2. Dimitrios Panagiotou, 2021. "Re-examining the leverage effect and gold’s safe haven properties with the utilization of the implied volatility of gold: a non-parametric quantile regression approach," SN Business & Economics, Springer, vol. 1(7), pages 1-18, July.
    3. Xiaorong Yang & Jia Chen & Degui Li & Runze Li, 2023. "Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure," Papers 2303.13218, arXiv.org.
    4. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
    5. Zheng Fang & Juwon Seo, 2021. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Econometrica, Econometric Society, vol. 89(5), pages 2439-2458, September.
    6. Chao, Shih-Kang & Härdle, Wolfgang Karl & Yuan, Ming, 2020. "Factorisable Multitask Quantile Regression," IRTG 1792 Discussion Papers 2020-004, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    7. Charlier, Isabelle & Paindaveine, Davy & Saracco, Jérôme, 2015. "Conditional quantile estimation based on optimal quantization: From theory to practice," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 20-39.
    8. Victor Chernozhukov & Ivan Fernandez-Val & Stefan Hoderlein & Hajo Holzmann & Whitney K. Newey, 2013. "Nonparametric identification in panels using quantiles," CeMMAP working papers 66/13, Institute for Fiscal Studies.
    9. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Some New Asymptotic Theory for Least Squares Series: Pointwise and Uniform Results," Papers 1212.0442, arXiv.org, revised Jun 2015.
    10. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Christian Pierdzioch, 2021. "El Nino, La Nina, and the Forecastability of the Realized Variance of Heating Oil Price Movements," Working Papers 202138, University of Pretoria, Department of Economics.
    11. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    12. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Gaussian approximation of suprema of empirical processes," CeMMAP working papers CWP75/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Alexandre Belloni & Victor Chernozhukov, 2009. "L1-Penalized quantile regression in high-dimensional sparse models," CeMMAP working papers CWP10/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Rangan Gupta & Syed Jawad Hussain Shahzad & Xin Sheng & Sowmya Subramaniam, 2023. "The role of oil and risk shocks in the high‐frequency movements of the term structure of interest rates: Evidence from the U.S. Treasury market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1845-1857, April.
    15. Manuel Arellano & Stéphane Bonhomme, 2016. "Nonlinear panel data methods for dynamic heterogeneous agent models," CeMMAP working papers 51/16, Institute for Fiscal Studies.
    16. Adam Rosen, 2009. "Set identification via quantile restrictions in short panels," CeMMAP working papers CWP26/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    17. Massimiliano Caporin & Petre Caraiani & Oguzhan Cepni & Rangan Gupta, 2024. "Predicting the Conditional Distribution of US Stock Market Systemic Stress: The Role of Climate Risks," Working Papers 202407, University of Pretoria, Department of Economics.
    18. Ferreira,Francisco H. G. & Firpo,Sergio & Galvao,Antonio F., 2017. "Estimation and inference for actual and counterfactual growth incidence curves," Policy Research Working Paper Series 7933, The World Bank.
    19. Giovanni Bonaccolto & Massimiliano Caporin & Rangan Gupta, 2015. "The Dynamic Impact of Uncertainty in Causing and Forecasting the Distribution of Oil Returns and Risk," Working Papers 201564, University of Pretoria, Department of Economics.
    20. Cattaneo, Matias D & Jansson, Michael & Ma, Xinwei, 2021. "Local regression distribution estimators," Department of Economics, Working Paper Series qt7416d3x8, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    21. Hao, Meiling & Lin, Yuanyuan & Shen, Guohao & Su, Wen, 2023. "Nonparametric inference on smoothed quantile regression process," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    22. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly & Kaspar Wüthrich, 2017. "Generic inference on quantile and quantile effect functions for discrete outcomes," CeMMAP working papers CWP23/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    23. Matias D. Cattaneo & Richard K. Crump & Max H. Farrell & Yingjie Feng, 2019. "On Binscatter," Papers 1902.09608, arXiv.org, revised Apr 2024.
    24. Arun Chandrasekhar & Victor Chernozhukov & Francesca Molinari & Paul Schrimpf, 2012. "Inference for best linear approximations to set identified functions," CeMMAP working papers CWP43/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    25. Gimenes, Nathalie & Guerre, Emmanuel, 2022. "Quantile regression methods for first-price auctions," Journal of Econometrics, Elsevier, vol. 226(2), pages 224-247.
    26. Lin, Fangzheng & Tang, Yanlin & Zhu, Zhongyi, 2020. "Weighted quantile regression in varying-coefficient model with longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    27. Grigory Franguridi & Bulat Gafarov & Kaspar Wüthrich, 2021. "Conditional Quantile Estimators: A Small Sample Theory," CESifo Working Paper Series 9046, CESifo.
    28. Antonio F. Galvao & Thomas Parker & Zhijie Xiao, 2021. "Bootstrap inference for panel data quantile regression," Papers 2111.03626, arXiv.org.
    29. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2014. "Uniform post selection inference for LAD regression and other Z-estimation problems," CeMMAP working papers 51/14, Institute for Fiscal Studies.
    30. Manuel Arellano & Stéphane Bonhomme, 2015. "Nonlinear panel data estimation via quantile regressions," CeMMAP working papers 40/15, Institute for Fiscal Studies.
    31. Rangan Gupta & Syed Jawad Hussain Shahzad & Xin Sheng & Sowmya Subramaniam, 2020. "The Role of Oil and Risk Shocks in the High-Frequency Movements of the Term Structure of Interest Rates of the United States," Working Papers 202063, University of Pretoria, Department of Economics.
    32. Zhongjun Qu & Jungmo Yoon, 2011. "Nonparametric Estimation and Inference on Conditional Quantile Processes," Boston University - Department of Economics - Working Papers Series WP2011-059, Boston University - Department of Economics.
    33. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Ivan Fernandez-Val, 2016. "Conditional quantile processes based on series or many regressors," CeMMAP working papers 46/16, Institute for Fiscal Studies.
    34. Matias D. Cattaneo & Yingjie Feng & Rocio Titiunik, 2019. "Prediction Intervals for Synthetic Control Methods," Papers 1912.07120, arXiv.org, revised Sep 2021.
    35. Manuel Arellano & Richard Blundell & Stéphane Bonhomme, 2015. "Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework," Working Papers wp2015_1506, CEMFI.
    36. Zheng Fang & Andres Santos & Azeem M. Shaikh & Alexander Torgovitsky, 2020. "Inference for Large-Scale Linear Systems with Known Coefficients," Papers 2009.08568, arXiv.org, revised Sep 2021.
    37. Zheng Fang, 2021. "A Unifying Framework for Testing Shape Restrictions," Papers 2107.12494, arXiv.org, revised Aug 2021.
    38. Elie Bouri & Rangan Gupta & Clement Kweku Kyei & Sowmya Subramaniam, 2020. "High-Frequency Movements of the Term Structure of Interest Rates of the United States: The Role of Oil Market Uncertainty," Working Papers 202085, University of Pretoria, Department of Economics.
    39. Vira Semenova, 2020. "Generalized Lee Bounds," Papers 2008.12720, arXiv.org, revised Feb 2023.
    40. Matias D. Cattaneo & Ricardo P. Masini & William G. Underwood, 2022. "Yurinskii's Coupling for Martingales," Papers 2210.00362, arXiv.org, revised Sep 2024.
    41. Shujie Ma & Oliver Linton & Jiti Gao, 2018. "Estimation in semiparametric quantile factor models," CeMMAP working papers CWP07/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    42. Richard Spady & Sami Stouli, 2012. "Dual Regression," Papers 1210.6958, arXiv.org, revised Sep 2018.
    43. Chen, Xirong & Li, Degui & Li, Qi & Li, Zheng, 2019. "Nonparametric estimation of conditional quantile functions in the presence of irrelevant covariates," Journal of Econometrics, Elsevier, vol. 212(2), pages 433-450.
    44. Zhipeng Liao & Xiaoxia Shi, 2020. "A nondegenerate Vuong test and post selection confidence intervals for semi/nonparametric models," Quantitative Economics, Econometric Society, vol. 11(3), pages 983-1017, July.
    45. Torossian, Léonard & Picheny, Victor & Faivre, Robert & Garivier, Aurélien, 2020. "A review on quantile regression for stochastic computer experiments," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    46. Chronopoulos, Ilias & Raftapostolos, Aristeidis & Kapetanios, George, 2023. "Forecasting Value-at-Risk using deep neural network quantile regression," Essex Finance Centre Working Papers 34837, University of Essex, Essex Business School.
    47. Arun Chandrasekhar & Victor Chernozhukov & Francesca Molinari & Paul Schrimpf, 2019. "Best linear approximations to set identified functions: with an application to the gender wage gap," CeMMAP working papers 09/19, Institute for Fiscal Studies.
    48. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers CWP70/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    49. Xi Chen & Victor Chernozhukov & Iv'an Fern'andez-Val & Scott Kostyshak & Ye Luo, 2018. "Shape-Enforcing Operators for Point and Interval Estimators," Papers 1809.01038, arXiv.org, revised Feb 2021.
    50. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Uniform post selection inference for LAD regression models," CeMMAP working papers 24/13, Institute for Fiscal Studies.
    51. Feng, Xingdong & Liu, Qiaochu & Wang, Caixing, 2023. "A lack-of-fit test for quantile regression process models," Statistics & Probability Letters, Elsevier, vol. 192(C).
    52. Tengyuan Liang, 2022. "Universal prediction band via semi‐definite programming," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1558-1580, September.
    53. Rangan Gupta & Damien Moodley, 2023. "Housing Search Activity and Quantiles-Based Predictability of Housing Price Movements in the United States," Working Papers 202335, University of Pretoria, Department of Economics.
    54. Mehmet Balcilar & Seyi Saint Akadiri & Rangan Gupta & Stephen M. Miller, 2019. "Partisan Conflict and Income Inequality in the United States: A Nonparametric Causality-in-Quantiles Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(1), pages 65-82, February.
    55. Byunghoon Kang, 2019. "Inference in Nonparametric Series Estimation with Specification Searches for the Number of Series Terms," Papers 1909.12162, arXiv.org, revised Feb 2020.
    56. Shujie Ma & Oliver Linton & Jiti Gao, 2017. "Estimation and inference in semiparametric quantile factor models," Monash Econometrics and Business Statistics Working Papers 8/17, Monash University, Department of Econometrics and Business Statistics.
    57. Zheng Fang & Juwon Seo, 2019. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Papers 1910.07689, arXiv.org, revised Sep 2021.
    58. Efang Kong & Oliver Linton & Yingcun Xia, 2011. "Global Bahadur representation for nonparametric censored regression quantiles and its applications," CeMMAP working papers CWP33/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    59. Victor Chernozhukov & Vira Semenova, 2018. "Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions," CeMMAP working papers CWP40/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    60. Qihui Chen, 2022. "Robust Estimation of Conditional Factor Models," Papers 2204.00801, arXiv.org, revised Apr 2022.
    61. He, Xuming & Pan, Xiaoou & Tan, Kean Ming & Zhou, Wen-Xin, 2023. "Smoothed quantile regression with large-scale inference," Journal of Econometrics, Elsevier, vol. 232(2), pages 367-388.
    62. Karen X. Yan & Qi Li, 2018. "Nonparametric Estimation of a Conditional Quantile Function in a Fixed Effects Panel Data Model," JRFM, MDPI, vol. 11(3), pages 1-10, August.
    63. Mehmet Balcilar & Seyi Saint Akadiri & Rangan Gupta & Stephen M. Miller, 2017. "Partisan Conflict and Income Distribution in the United States: A Nonparametric Causality-in-Quantiles Approach," Working papers 2017-11, University of Connecticut, Department of Economics.
    64. Holly Brannelly & Andrea Macrina & Gareth W. Peters, 2021. "Stochastic measure distortions induced by quantile processes for risk quantification and valuation," Papers 2201.02045, arXiv.org.
    65. Erik Figueiredo & Luiz Lima & Georg Schaur, 2016. "The effect of the Euro on the bilateral trade distribution," Empirical Economics, Springer, vol. 50(1), pages 17-29, February.

  119. Xiaohong Chen & Victor Chernozhukov & Sokbae (Simon) Lee & Whitney K. Newey, 2011. "Local identification of nonparametric and semiparametric models," CeMMAP working papers 17/11, Institute for Fiscal Studies.

    Cited by:

    1. Xiaohong Chen & Andres Santos, 2015. "Overidentification in Regular Models," Cowles Foundation Discussion Papers 1999R, Cowles Foundation for Research in Economics, Yale University, revised Jun 2018.
    2. Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2017. "Smoothed GMM for quantile models," Papers 1707.03436, arXiv.org, revised Feb 2018.
    3. Yu Zhu, 2020. "Inference in nonparametric/semiparametric moment equality models with shape restrictions," Quantitative Economics, Econometric Society, vol. 11(2), pages 609-636, May.
    4. Dalderop, Jeroen, 2023. "Semiparametric estimation of latent variable asset pricing models," Journal of Econometrics, Elsevier, vol. 236(1).
    5. Chen, Qihui, 2021. "Robust and optimal estimation for partially linear instrumental variables models with partial identification," Journal of Econometrics, Elsevier, vol. 221(2), pages 368-380.
    6. Xiaohong Chen & Demian Pouzo, 2014. "Sieve Wald and QLR Inferences on Semi/nonparametric Conditional Moment Models," Papers 1411.1144, arXiv.org, revised Mar 2015.
    7. Yingyao Hu & Ji‐Liang Shiu, 2018. "Identification and estimation of semi‐parametric censored dynamic panel data models of short time periods," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 55-85, February.
    8. Matzkin, Rosa L., 2016. "On independence conditions in nonseparable models: Observable and unobservable instruments," Journal of Econometrics, Elsevier, vol. 191(2), pages 302-311.
    9. Xiaohong Chen & Demian Pouzo, 2014. "Sieve Wald and QLR Inferences on Semi/nonparametric Conditional Moment Models," CeMMAP working papers 38/14, Institute for Fiscal Studies.
    10. Timothy M. Christensen, 2014. "Nonparametric identification of positive eigenfunctions," CeMMAP working papers 37/14, Institute for Fiscal Studies.
    11. Hidehiko Ichimura & Whitney K. Newey, 2017. "The influence function of semiparametric estimators," CeMMAP working papers 06/17, Institute for Fiscal Studies.
    12. Patrick Bajari & Chenghuan Sean Chu & Denis Nekipelov & Minjung Park, 2016. "Identification and semiparametric estimation of a finite horizon dynamic discrete choice model with a terminating action," Quantitative Marketing and Economics (QME), Springer, vol. 14(4), pages 271-323, December.
    13. Victor Chernozhukov & Christian Hansen, 2013. "Quantile models with endogeneity," CeMMAP working papers CWP25/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Cazals, Catherine & Feve, Frederique & Florens, Jean-Pierre & Simar, Leopold, 2016. "Non Parametric Instrumental Variables Estimation for Efficiency Frontier," LIDAM Reprints ISBA 2016014, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    15. Jason R. Blevins & Wei Shi & Donald R. Haurin & Stephanie Moulton, 2020. "A Dynamic Discrete Choice Model Of Reverse Mortgage Borrower Behavior," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(4), pages 1437-1477, November.
    16. Dunker, Fabian & Florens, Jean-Pierre & Hohage, Thorsten & Johannes, Jan & Mammen, Enno, 2014. "Iterative estimation of solutions to noisy nonlinear operator equations in nonparametric instrumental regression," LIDAM Reprints ISBA 2014007, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    17. Marcin Penconek & Paweł Strawiński, 2022. "Generalisation of the gross flows model," Working Papers 2022-17, Faculty of Economic Sciences, University of Warsaw.
    18. Victor Chernozhukov & Whitney K. Newey & Andres Santos, 2015. "Constrained conditional moment restriction models," CeMMAP working papers 59/15, Institute for Fiscal Studies.
    19. Jaroslav Borovička & Lars P. Hansen & José A. Scheinkman, 2014. "Misspecified Recovery," NBER Working Papers 20209, National Bureau of Economic Research, Inc.
      • Jaroslav Borovička & Lars Peter Hansen & José A. Scheinkman, 2016. "Misspecified Recovery," Journal of Finance, American Finance Association, vol. 71(6), pages 2493-2544, December.
      • Jaroslav Borovicka & Lars Peter Hansen & Jose A. Scheinkman, 2015. "Misspecified Recovery," Working Papers 063_2014, Princeton University, Department of Economics, Econometric Research Program..
      • Jaroslav Boroviv{c}ka & Lars Peter Hansen & Jos'e A. Scheinkman, 2014. "Misspecified Recovery," Papers 1412.0042, arXiv.org, revised Oct 2015.
    20. Victor H. Aguiar & Nail Kashaev, 2018. "Stochastic Revealed Preferences with Measurement Error," Papers 1810.05287, arXiv.org, revised Sep 2020.
    21. Chen, Xiaohong & Pouzo, Demian & Powell, James L., 2019. "Penalized sieve GEL for weighted average derivatives of nonparametric quantile IV regressions," Journal of Econometrics, Elsevier, vol. 213(1), pages 30-53.
    22. Enache, Andreea & Florens, Jean-Pierre, 2024. "Quantile analysis of “hazard-rate” game models," Journal of Econometrics, Elsevier, vol. 238(2).
    23. Loh, Isaac, 2023. "Genericity of the completeness condition with constrained instruments," Economics Letters, Elsevier, vol. 224(C).
    24. Doug J. Chung & Byungyeon Kim & Byoung G. Park, 2021. "The Comprehensive Effects of Sales Force Management: A Dynamic Structural Analysis of Selection, Compensation, and Training," Management Science, INFORMS, vol. 67(11), pages 7046-7074, November.
    25. Andrew Chesher & Adam Rosen, 2014. "Generalized instrumental variable models," CeMMAP working papers 04/14, Institute for Fiscal Studies.
    26. Christoph Breunig, 2019. "Specification Testing in Nonparametric Instrumental Quantile Regression," Papers 1909.10129, arXiv.org.
    27. Feng, Junlong, 2024. "Matching points: Supplementing instruments with covariates in triangular models," Journal of Econometrics, Elsevier, vol. 238(1).
    28. Ben Deaner, 2018. "Proxy Controls and Panel Data," Papers 1810.00283, arXiv.org, revised Nov 2023.
    29. Jean-Jacques Forneron, 2019. "A Sieve-SMM Estimator for Dynamic Models," Papers 1902.01456, arXiv.org, revised Jan 2023.
    30. Fabian Dunker, 2015. "Adaptive estimation for some nonparametric instrumental variable models," Papers 1511.03977, arXiv.org, revised Aug 2021.
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    32. Timothy M. Christensen, 2014. "Nonparametric identification of positive eigenfunctions," CeMMAP working papers CWP37/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    33. Patrick Bajari & Chenghuan Sean Chu & Denis Nekipelov & Minjung Park, 2013. "A Dynamic Model of Subprime Mortgage Default: Estimation and Policy Implications," NBER Working Papers 18850, National Bureau of Economic Research, Inc.
    34. Pierre-Andre Chiappori & Ivana Komunjer & Dennis Kristensen, 2011. "Nonparametric Identification and Estimation of Transformation Models," CAM Working Papers 2011-01, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
    35. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.
    36. Senay Sokullu & Irene Botosaru & Chris Muris, 2022. "Time-Varying Linear Transformation Models with Fixed Effects and Endogeneity for Short Panels," Bristol Economics Discussion Papers 22/756, School of Economics, University of Bristol, UK.
    37. Hiroaki Kaido & Kaspar Wüthrich, 2018. "Decentralization estimators for instrumental variable quantile regression models," CeMMAP working papers CWP72/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    38. Breunig, Christoph, 2016. "Specification testing in nonparametric instrumental quantile regression," SFB 649 Discussion Papers 2016-032, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    39. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2023. "Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness," Papers 2302.05404, arXiv.org.
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    41. de Castro, Luciano & Cundy, Lance D. & Galvao, Antonio F. & Westenberger, Rafael, 2023. "A dynamic quantile model for distinguishing intertemporal substitution from risk aversion," European Economic Review, Elsevier, vol. 159(C).
    42. Luciano de Castro & Antonio F. Galvao & David M. Kaplan, 2017. "Smoothed instrumental variables quantile regression, with estimation of quantile Euler equations," Working Papers 1710, Department of Economics, University of Missouri, revised 28 Feb 2018.
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    Cited by:

    1. Chen, Songnian, 2018. "Sequential estimation of censored quantile regression models," Journal of Econometrics, Elsevier, vol. 207(1), pages 30-52.
    2. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey & Sami Stouli & Francis Vella, 2017. "Semiparametric estimation of structural functions in nonseparable triangular models," CeMMAP working papers CWP48/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Alina Ozhegova & Evgeniy M. Ozhegov, 2018. "Heterogeneity in demand for performances and seats in the theatre," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(3), pages 131-145, June.
    4. Olga Miroshnichenko & Elena Iakovleva & Natalia Voronova, 2022. "Banking Sector Profitability: Does Household Income Matter?," Sustainability, MDPI, vol. 14(6), pages 1-19, March.
    5. Yangyang Chen & Rui Ge & Henock Louis & Leon Zolotoy, 2019. "Stock liquidity and corporate tax avoidance," Review of Accounting Studies, Springer, vol. 24(1), pages 309-340, March.
    6. Erik Figueiredo & Luiz Renato Lima & Gianluca Orefice, 2016. "Migration and Regional Trade Agreements: A (New) Gravity Estimation," Review of International Economics, Wiley Blackwell, vol. 24(1), pages 99-125, February.
    7. Fernandez-Val , Ivan & van Vuuren, Aico & Vella, Francis, 2018. "Nonseparable Sample Selection Models with Censored Selection Rules," Working Papers in Economics 716, University of Gothenburg, Department of Economics.
    8. Javier Alejo & Antonio F. Galvao & Julian Martinez-Iriarte & Gabriel Montes-Rojas, 2023. "Unconditional Quantile Partial Effects via Conditional Quantile Regression," Papers 2301.07241, arXiv.org, revised Dec 2023.
    9. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    10. Kim, Young-Joo & Daly, Vincent, 2019. "The Education Gradient in Health: The Case of Obesity in the UK and US," Economics Discussion Papers 2019-4, School of Economics, Kingston University London.
    11. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2020. "Unconditional Quantile Regression with High Dimensional Data," Papers 2007.13659, arXiv.org, revised Feb 2022.
    12. Jaeun Choi & A. James O'Malley, 2017. "Estimating the causal effect of treatment in observational studies with survival time end points and unmeasured confounding," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 159-185, January.
    13. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly, 2012. "Inference on counterfactual distributions," CeMMAP working papers 05/12, Institute for Fiscal Studies.
    14. L. Benfratello & A. Bottasso & C. Piccardo, 2022. "R&D and export performance: exploring heterogeneity along the export intensity distribution," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(2), pages 189-232, June.
    15. Männasoo, Kadri, 2022. "Working hours and gender wage differentials: Evidence from the American Working Conditions Survey," Labour Economics, Elsevier, vol. 76(C).
    16. Konstantinos Konstantaras & Vasilios Sogiakas, 2019. "Is stock liquidity transferred and upgraded in acquisitions? Evidence from liquidity synergies in US freeze-outs," Annals of Operations Research, Springer, vol. 282(1), pages 179-216, November.
    17. Victor Chernozhukov & Ivan Fernández-Val & Sukjin Han & Amanda Kowalski, 2019. "Censored quantile instrumental-variable estimation with Stata," Stata Journal, StataCorp LP, vol. 19(4), pages 768-781, December.
    18. Amanda E. Kowalski, 2016. "Doing More When You're Running LATE: Applying Marginal Treatment Effect Methods to Examine Treatment Effect Heterogeneity in Experiments," NBER Working Papers 22363, National Bureau of Economic Research, Inc.
    19. Raslan Alzuabi & Sarah Brown & Daniel Gray & Mark N Harris & Christopher Spencer, 2022. "Household saving, health, and healthcare utilization in Japan [Stature, obesity, and portfolio choice]," Oxford Economic Papers, Oxford University Press, vol. 74(2), pages 473-497.
    20. Bo Wei & Limin Peng & Mei‐Jie Zhang & Jason P. Fine, 2021. "Estimation of causal quantile effects with a binary instrumental variable and censored data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 559-578, July.
    21. Mukasa Adamon N. & Anthony M. Simpasa & Adeleke Oluwole Salami, 2017. "Working Paper 247 - Credit constraints and farm productivity: Micro-level evidence from smallholder farmers in Ethiopia," Working Paper Series 2356, African Development Bank.
    22. Marinho Bertanha & Andrew H. McCallum & Nathan Seegert, 2021. "Better Bunching, Nicer Notching," Finance and Economics Discussion Series 2021-002, Board of Governors of the Federal Reserve System (U.S.).
    23. Leon Zolotoy & Don O’Sullivan & Geoffrey P. Martin & Robert M. Wiseman, 2021. "Stakeholder Agency Relationships: CEO Stock Options and Corporate Tax Avoidance," Journal of Management Studies, Wiley Blackwell, vol. 58(3), pages 782-814, May.
    24. Whitney Newey & Sami Stouli, 2018. "Control Variables, Discrete Instruments, and Identification of Structural Functions," Bristol Economics Discussion Papers 18/702, School of Economics, University of Bristol, UK.
    25. Jad Beyhum & Jean-Pierre FLorens & Ingrid Van Keilegom, 2020. "Nonparametric instrumental regression with right censored duration outcomes," Papers 2011.10423, arXiv.org.
    26. Jad Beyhum, 2021. "Two-stage least squares with a randomly right censored outcome," Papers 2110.05107, arXiv.org.
    27. Mendolia, Silvia & Paloyo, Alfredo R. & Walker, Ian, 2016. "Heterogeneous effects of high school peers on educational outcomes," Ruhr Economic Papers 612, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    28. Jad Beyhum & Lorenzo Tedesco & Ingrid Van Keilegom, 2022. "Instrumental variable quantile regression under random right censoring," Papers 2209.01429, arXiv.org, revised Feb 2023.
    29. Jad Beyhum & Jean-Pierre Florens & Ingrid Van Keilegom, 2021. "A nonparametric instrumental approach to endogeneity in competing risks models," Papers 2105.00946, arXiv.org.
    30. Aldieri, Luigi & Vinci, Concetto Paolo, 2017. "Quantile regression for Panel data: An empirical approach for knowledge spillovers endogeneity," MPRA Paper 76405, University Library of Munich, Germany.
    31. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    32. Evgeniy M. Ozhegov & Alina Ozhegova, 2020. "Regression tree model for prediction of demand with heterogeneity and censorship," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 489-500, April.
    33. Chen, Songnian, 2019. "Quantile regression for duration models with time-varying regressors," Journal of Econometrics, Elsevier, vol. 209(1), pages 1-17.
    34. Yinchu Zhu, 2018. "Learning non-smooth models: instrumental variable quantile regressions and related problems," Papers 1805.06855, arXiv.org, revised Sep 2019.
    35. Justin Falk, 2023. "The Effects of Work Requirements on the Employment and Income of TANF Participants: Working Paper 2023-03," Working Papers 58867, Congressional Budget Office.
    36. Manuel Arellano & Stéphane Bonhomme, 2017. "Sample Selection in Quantile Regression: A Survey," Working Papers wp2017_1702, CEMFI.
    37. James E. Payne & James W. Saunoris, 2020. "Corruption and Firm Tax Evasion in Transition Economies: Results from Censored Quantile Instrumental Variables Estimation," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 48(2), pages 195-206, June.
    38. Evgeniy M. Ozhegov & Daria Teterina, 2018. "The Ensemble Method For Censored Demand Prediction," HSE Working papers WP BRP 200/EC/2018, National Research University Higher School of Economics.
    39. Pourya Valizadeh & Shu Wen Ng, 2021. "Would A National Sugar‐Sweetened Beverage Tax in the United States Be Well Targeted?," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 961-986, May.
    40. Brown, Sarah & Taylor, Karl, 2015. "Charitable Behaviour and the Big Five Personality Traits: Evidence from UK Panel Data," IZA Discussion Papers 9318, Institute of Labor Economics (IZA).
    41. Schmillen, Achim & Umkehrer, Matthias, 2013. "The scars of youth : effects of early-career unemployment on future unemployment experience," IAB-Discussion Paper 201306, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    42. Fernández-Val, Iván & van Vuuren, Aico & Vella, Francis, 2018. "Nonseparable Sample Selection Models with Censored Selection Rules: An Application to Wage Decompositions," IZA Discussion Papers 11294, Institute of Labor Economics (IZA).
    43. Abi Adams & Richard Blundell & Martin Browning & Ian Crawford, 2015. "Prices versus preferences: taste change and revealed preference," IFS Working Papers W15/11, Institute for Fiscal Studies.
    44. Virginie Vial & Julien Hanoteau, 2015. "Returns to Micro-Entrepreneurship in an Emerging Economy: A Quantile Study of Entrepreneurial Indonesian Households’ Welfare," Post-Print hal-01457392, HAL.
    45. Roger, Muriel & Savignac, Frédérique & d’Addio, Anna, 2020. "Pensions and household savings: cross-country heterogeneity in Europe," Working Paper Series 2372, European Central Bank.
    46. Basha, Shabeen Afsar & Bennasr, Hamdi & Goaied, Mohamed, 2023. "Financial literacy, financial development, and leverage of small firms," International Review of Financial Analysis, Elsevier, vol. 86(C).
    47. Thomas Bassetti & Filippo Pavesi, 2015. "Electoral Contributions and the Cost of Unpopularity," Working Papers 05/2015, University of Verona, Department of Economics.
    48. Jayeeta Bhattacharya, 2020. "Quantile regression with generated dependent variable and covariates," Papers 2012.13614, arXiv.org.
    49. Su Liangjun & Tadao Hoshino, 2015. "Sieve Instrumental Variable Quantile Regression Estimation of Functional Coefficient Models," Working Papers 01-2015, Singapore Management University, School of Economics.
    50. Chen, Songnian & Wang, Qian, 2020. "Semiparametric estimation of a censored regression model with endogeneity," Journal of Econometrics, Elsevier, vol. 215(1), pages 239-256.
    51. Lee, Sungwon, 2023. "Efficient estimation of a triangular system of equations for quantile regression," Economics Letters, Elsevier, vol. 226(C).
    52. Evgeniy M. Ozhegov & Alina Ozhegova, 2017. "Regression Tree Model for Analysis of Demand with Heterogeneity and Censorship," HSE Working papers WP BRP 174/EC/2017, National Research University Higher School of Economics.
    53. David M. Kaplan, 2020. "sivqr: Smoothed IV quantile regression," Working Papers 2009, Department of Economics, University of Missouri.
    54. Beyhum, Jad & Florens, Jean-Pierre & Van Keilegom, Ingrid, 2020. "Nonparametric Instrumental Regression with Right Censored Duration Outcomes," TSE Working Papers 20-1164, Toulouse School of Economics (TSE).
    55. Haowen Bao & Zongwu Cai & Yuying Sun & Shouyang Wang, 2023. "Penalized Model Averaging for High Dimensional Quantile Regressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202302, University of Kansas, Department of Economics, revised Jan 2023.
    56. Jau-er Chen & Masanori Kashiwagi, 2017. "The Japanese Taylor rule estimated using censored quantile regressions," Empirical Economics, Springer, vol. 52(1), pages 357-371, February.
    57. Li, Tong & Oka, Tatsushi, 2015. "Set identification of the censored quantile regression model for short panels with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 363-377.
    58. Victor Champonnois & Olivier Chanel, 2016. "How useful are (Censored) Quantile Regressions for Contingent Valuation?," Working Papers 2016.12, FAERE - French Association of Environmental and Resource Economists.
    59. Tatsushi Oka & Ken Yamada, 2023. "Heterogeneous Impact of the Minimum Wage: Implications for Changes in Between- and Within-Group Inequality," Journal of Human Resources, University of Wisconsin Press, vol. 58(1), pages 335-362.
    60. Sanna Nivakoski, 2020. "Wealth and the effect of subjective survival probability," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(2), pages 633-670, April.
    61. Amanda E. Kowalski, 2018. "Extrapolation using Selection and Moral Hazard Heterogeneity from within the Oregon Health Insurance Experiment," Cowles Foundation Discussion Papers 2135, Cowles Foundation for Research in Economics, Yale University.
    62. Victor Champonnois & Olivier Chanel & Costin Protopopescu, 2022. "Quantile Regression Analysis of Censored Data with Selection An Application to Willingness-to-Pay Data," AMSE Working Papers 2214, Aix-Marseille School of Economics, France.
    63. Blanco, German & Chen, Xuan & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2018. "Bounds on Average and Quantile Treatment Effects on Duration Outcomes under Censoring, Selection, and Noncompliance," GLO Discussion Paper Series 288, Global Labor Organization (GLO).
    64. Jorge V Pérez-Rodríguez & Francisco Ledesma-Rodríguez, 2021. "Unconditional quantile regression and tourism expenditure: The case of the Canary Islands," Tourism Economics, , vol. 27(4), pages 626-648, June.
    65. Bucheli, José R. & Fontenla, Matías & Waddell, Benjamin James, 2019. "Return migration and violence," World Development, Elsevier, vol. 116(C), pages 113-124.
    66. Lima, Luiz Renato & Mesquita, Shirley & Wanamaker, Marianne, 2015. "Child labor and the wealth paradox: The role of altruistic parents," Economics Letters, Elsevier, vol. 130(C), pages 80-82.
    67. Alina Buzanakova & Evgeniy M. Ozhegov, 2016. "Demand for performing arts: the effect of unobserved quality on price elasticity," ACEI Working Paper Series AWP-05-2016, Association for Cultural Economics International, revised Jun 2016.
    68. De Silva, Dakshina G. & Kosmopoulou, Georgia & Lamarche, Carlos, 2017. "Subcontracting and the survival of plants in the road construction industry: A panel quantile regression analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 137(C), pages 113-131.
    69. Aziz N. Berdiev & James W. Saunoris, 2023. "The case for independence: Does central bank independence curb the spread of the underground economy?," Kyklos, Wiley Blackwell, vol. 76(3), pages 407-435, August.
    70. Amanda E. Kowalski, 2016. "Doing More When You're Running LATE: Applying Marginal Treatment Effect Methods to Examine Treatment Effect Heterogeneity in Experiments for the Young and Privately Insured"," Cowles Foundation Discussion Papers 2045, Cowles Foundation for Research in Economics, Yale University.
    71. Chen, Songnian & Wang, Qian, 2023. "Quantile regression with censoring and sample selection," Journal of Econometrics, Elsevier, vol. 234(1), pages 205-226.
    72. Broberg, Thomas & Kazukauskas, Andrius, 2014. "Inefficiencies in residential use of energy - A critical overview of literature and energy efficiency policies in EU and Sweden," CERE Working Papers 2014:7, CERE - the Center for Environmental and Resource Economics.
    73. Affuso, Ermanno & Hite, Diane, 2013. "A model for sustainable land use in biofuel production: An application to the state of Alabama," Energy Economics, Elsevier, vol. 37(C), pages 29-39.
    74. Sugimoto, Kota, 2021. "Ownership versus legal unbundling of electricity transmission network: Evidence from renewable energy investment in Germany," Energy Economics, Elsevier, vol. 99(C).
    75. Viola, Alessandra Pasqualina & Klotzle, Marcelo Cabus & Pinto, Antonio Carlos Figueiredo & da Silveira Barbedo, Claudio Henrique, 2019. "Foreign exchange interventions in Brazil and their impact on volatility: A quantile regression approach," Research in International Business and Finance, Elsevier, vol. 47(C), pages 251-263.
    76. Kiran Krishnamurthy, Chandra & Kriström, Bengt, 2013. "Determinants of the price-premium for Green Energy: Evidence from an OECD cross-section," CERE Working Papers 2013:7, CERE - the Center for Environmental and Resource Economics, revised 30 Jun 2014.
    77. J Doran & G Ryan, 2016. "The effectiveness of R&D and external interaction for innovation: Insights from quantile regression," Economic Issues Journal Articles, Economic Issues, vol. 21(1), pages 47-65, March.
    78. Liqiong Chen & Antonio F. Galvao & Suyong Song, 2021. "Quantile Regression with Generated Regressors," Econometrics, MDPI, vol. 9(2), pages 1-35, April.
    79. Holland, Sara B. & Sarkissian, Sergei & Schill, Michael J. & Warnock, Francis E., 2024. "Nonlinearities and a pecking order in cross-border investment," Journal of Banking & Finance, Elsevier, vol. 166(C).
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    Cited by:

    1. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    2. Caner, Mehmet & Kock, Anders Bredahl, 2018. "Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso," Journal of Econometrics, Elsevier, vol. 203(1), pages 143-168.
    3. Áureo de Paula & Imran Rasul & Pedro CL Souza, 2023. "Identifying network ties from panel data: Theory and an application to tax competition," CeMMAP working papers 21/23, Institute for Fiscal Studies.
    4. Liqian Cai & Arnab Bhattacharjee & Roger Calantone & Taps Maiti, 2019. "Variable Selection with Spatially Autoregressive Errors: A Generalized Moments LASSO Estimator," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 146-200, September.
    5. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023. "Granger Causality Testing in High-Dimensional VARs: A Post-Double-Selection Procedure," Journal of Financial Econometrics, Oxford University Press, vol. 21(3), pages 915-958.
    6. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, 2016. "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
    7. Bottmer, Lea & Croux, Christophe & Wilms, Ines, 2022. "Sparse regression for large data sets with outliers," European Journal of Operational Research, Elsevier, vol. 297(2), pages 782-794.
    8. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    9. Mueller, Hannes & Rauh, Christopher, 2016. "Reading Between the Lines: Prediction of Political Violence Using Newspaper Text," CEPR Discussion Papers 11516, C.E.P.R. Discussion Papers.
    10. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2021. "Measurement of factor strength: Theory and practice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 587-613, August.
    11. Matteo Barigozzi & Christian Brownlees, 2013. "Nets: Network Estimation for Time Series," Working Papers 723, Barcelona School of Economics.
    12. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    13. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Daniels, David P. & Zlatev, Julian J., 2019. "Choice architects reveal a bias toward positivity and certainty," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 132-149.
    15. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers 62/13, Institute for Fiscal Studies.
    16. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    17. Aglasan, Serkan & Goodwin, Barry K. & Rejesus, Roderick, 2020. "Genetically Modified Rootworm-Resistant Corn, Risk, and Weather: Evidence from High Dimensional Methods," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 305181, Agricultural and Applied Economics Association.
    18. Gambacorta, Leonardo & Huang, Yiping & Qiu, Han & Wang, Jingyi, 2019. "How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm," CEPR Discussion Papers 14259, C.E.P.R. Discussion Papers.
    19. Bonan, Jacopo & Burlacu, Sergiu & Galliera, Arianna, 2023. "Prosociality in variants of the dictator game: Evidence from children in El Salvador," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 107(C).
    20. Federico A. Bugni & Mehmet Caner & Anders Bredahl Kock & Soumendra Lahiri, 2016. "Inference in partially identified models with many moment inequalities using Lasso," CREATES Research Papers 2016-12, Department of Economics and Business Economics, Aarhus University.
    21. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio, 2017. "Economic Predictions with Big Data: The Illusion Of Sparsity," CEPR Discussion Papers 12256, C.E.P.R. Discussion Papers.
    22. Anders Bredahl Kock & Laurent A.F. Callot, 2012. "Oracle Inequalities for High Dimensional Vector Autoregressions," CREATES Research Papers 2012-16, Department of Economics and Business Economics, Aarhus University.
    23. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Sparse Graphical Vector Autoregression: A Bayesian Approach," Annals of Economics and Statistics, GENES, issue 123-124, pages 333-361.
    24. Song Song & Peter J. Bickel, 2011. "Large Vector Auto Regressions," Papers 1106.3915, arXiv.org.
    25. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
    26. André Nunes Maranhão, 2024. "Brazilian Business Cycle Analysis in a High-Dimensional and Time-Irregular Span Context," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 20(1), pages 1-58, August.
    27. Carlos A. Manzanares & Ying Jiang & Patrick Bajari, 2015. "Improving Policy Functions in High-Dimensional Dynamic Games," NBER Working Papers 21124, National Bureau of Economic Research, Inc.
    28. André Nunes Maranhão & Nicole Rennó Castro, 2023. "Dissecting Brazilian agriculture business cycles in high-dimensional and time-irregular span contexts," Empirical Economics, Springer, vol. 65(4), pages 1543-1578, October.
    29. Myrto Kalouptsidi, 2014. "Time to Build and Fluctuations in Bulk Shipping," American Economic Review, American Economic Association, vol. 104(2), pages 564-608, February.
    30. Alec Smith & B. Douglas Bernheim & Colin Camerer & Antonio Rangel, 2013. "Neural Activity Reveals Preferences Without Choices," NBER Working Papers 19270, National Bureau of Economic Research, Inc.
    31. Xu, Ning & Hong, Jian & Fisher, Timothy, 2016. "Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso," MPRA Paper 71670, University Library of Munich, Germany.
    32. Gold, David & Lederer, Johannes & Tao, Jing, 2020. "Inference for high-dimensional instrumental variables regression," Journal of Econometrics, Elsevier, vol. 217(1), pages 79-111.
    33. Lim, Dennis & Wang, Wenjie & Zhang, Yichong, 2024. "A conditional linear combination test with many weak instruments," Journal of Econometrics, Elsevier, vol. 238(2).
    34. Yichen Gao & Yu Zhang & Ximing Wu, 2015. "Penalized exponential series estimation of copula densities with an application to intergenerational dependence of body mass index," Empirical Economics, Springer, vol. 48(1), pages 61-81, February.
    35. Scoles, Brooke & Nicodemo, Catia, 2022. "Doctors’ attitudes toward specific medical conditions," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 182-199.
    36. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers CWP70/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    37. Awijen, Haithem & Ben Zaied, Younes & Ben Lahouel, Béchir & Khlifi, Foued, 2023. "Machine learning for US cross-industry return predictability under information uncertainty," Research in International Business and Finance, Elsevier, vol. 64(C).
    38. Guo, Zijian & Kang, Hyunseung & Cai, T. Tony & Small, Dylan S., 2018. "Testing endogeneity with high dimensional covariates," Journal of Econometrics, Elsevier, vol. 207(1), pages 175-187.
    39. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    40. Kock, Anders Bredahl, 2016. "Oracle inequalities, variable selection and uniform inference in high-dimensional correlated random effects panel data models," Journal of Econometrics, Elsevier, vol. 195(1), pages 71-85.
    41. Sander Gerritsen & Mark Kattenberg & Sonny Kuijpers, 2019. "The impact of age at arrival on education and mental health," CPB Discussion Paper 389, CPB Netherlands Bureau for Economic Policy Analysis.
    42. Demian Pouzo, 2015. "On the Non-Asymptotic Properties of Regularized M-estimators," Papers 1512.06290, arXiv.org, revised Oct 2016.
    43. De La Maza, Cristóbal & Davis, Alex & Azevedo, Inês, 2021. "Welfare analysis of the ecological impacts of electricity production in Chile using the sparse multinomial logit model," Ecological Economics, Elsevier, vol. 184(C).
    44. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    45. Damian Kozbur, 2013. "Inference in additively separable models with a high-dimensional set of conditioning variables," ECON - Working Papers 284, Department of Economics - University of Zurich, revised Apr 2018.
    46. Ng, Serena, 2013. "Variable Selection in Predictive Regressions," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 752-789, Elsevier.
    47. Huseynov, Samir & Palma, Marco A., 2018. "Does California’s LCFS Reduce CO2 Emissions?," 2018 Annual Meeting, August 5-7, Washington, D.C. 274200, Agricultural and Applied Economics Association.
    48. Victor Chernozhukov & Chris Hansen & Martin Spindler, 2016. "High-Dimensional Metrics in R," Papers 1603.01700, arXiv.org, revised Aug 2016.
    49. Riccardo Di Francesco, 2023. "Ordered Correlation Forest," Papers 2309.08755, arXiv.org.
    50. Zhu, Ying, 2018. "Sparse linear models and l1-regularized 2SLS with high-dimensional endogenous regressors and instruments," Journal of Econometrics, Elsevier, vol. 202(2), pages 196-213.
    51. Bo Cowgill & Patryk Perkowski, 2024. "Delegation in Hiring: Evidence from a Two-Sided Audit," Journal of Political Economy Microeconomics, University of Chicago Press, vol. 2(4), pages 852-882.
    52. Florens, Jean-Pierre & Van Bellegem, Sébastien, 2015. "Instrumental variable estimation in functional linear models," Journal of Econometrics, Elsevier, vol. 186(2), pages 465-476.
    53. Chatterjee, A. & Gupta, S. & Lahiri, S.N., 2015. "On the residual empirical process based on the ALASSO in high dimensions and its functional oracle property," Journal of Econometrics, Elsevier, vol. 186(2), pages 317-324.
    54. Youngjoo Cho & Debashis Ghosh, 2021. "Quantile-Based Subgroup Identification for Randomized Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 90-128, April.
    55. Li, Zhaoyuan & Yao, Jianfeng, 2019. "Testing for heteroscedasticity in high-dimensional regressions," Econometrics and Statistics, Elsevier, vol. 9(C), pages 122-139.
    56. Matthew Gentzkow & Bryan T. Kelly & Matt Taddy, 2017. "Text as Data," NBER Working Papers 23276, National Bureau of Economic Research, Inc.
    57. Dai, Wei & Tsang, Ka Wai, 2023. "A resampling approach for confidence intervals in linear time-series models after model selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).

  122. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Estimation of treatment effects with high-dimensional controls," CeMMAP working papers 42/11, Institute for Fiscal Studies.

    Cited by:

    1. Chen, Daniel L., 2016. "Priming Ideology: Why Presidential Elections Affect U.S. Judges," IAST Working Papers 16-39, Institute for Advanced Study in Toulouse (IAST), revised Aug 2016.
    2. Shi, Zhentao, 2016. "Econometric estimation with high-dimensional moment equalities," Journal of Econometrics, Elsevier, vol. 195(1), pages 104-119.
    3. Chen, Daniel L. & Michaeli, Moti & Spiro, Daniel, 2016. "Ideological Perfectionism," TSE Working Papers 16-694, Toulouse School of Economics (TSE).

  123. Victor Chernozhukov & Ivan Fernandez-Val & Alfred Galichon, 2010. "Quantile and Probability Curves without Crossing," SciencePo Working papers Main hal-01052958, HAL.

    Cited by:

    1. Romuald Meango & Esther Mirjam Girsberger, 2023. "Identification of Ex ante Returns Using Elicited Choice Probabilities: an Application to Preferences for Public-sector Jobs," Papers 2303.03009, arXiv.org, revised Jun 2024.
    2. Nicodemo, Catia & Raya, Josep M., 2012. "Change in the Distribution of House Prices across Spanish Cities," IZA Discussion Papers 6503, Institute of Labor Economics (IZA).
    3. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "On the asymptotic theory for least squares series: pointwise and uniform results," CeMMAP working papers 73/13, Institute for Fiscal Studies.
    4. Ivan Fernandez-Val & Hugo Freeman & Martin Weidner, 2020. "Low-rank approximations of nonseparable panel models," CeMMAP working papers CWP52/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Gaglianone, Wagner Piazza & Guillén, Osmani Teixeira de Carvalho & Figueiredo, Francisco Marcos Rodrigues, 2018. "Estimating inflation persistence by quantile autoregression with quantile-specific unit roots," Economic Modelling, Elsevier, vol. 73(C), pages 407-430.
    6. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey & Sami Stouli & Francis Vella, 2017. "Semiparametric estimation of structural functions in nonseparable triangular models," CeMMAP working papers CWP48/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    8. Holger Dette & Stanislav Volgushev, 2008. "Non‐crossing non‐parametric estimates of quantile curves," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 609-627, July.
    9. Chao, Shih-Kang & Härdle, Wolfgang Karl & Yuan, Ming, 2020. "Factorisable Multitask Quantile Regression," IRTG 1792 Discussion Papers 2020-004, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    10. Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
    11. Charlier, Isabelle & Paindaveine, Davy & Saracco, Jérôme, 2015. "Conditional quantile estimation based on optimal quantization: From theory to practice," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 20-39.
    12. Christian Manuel Posso Suárez, 2008. "Desigualdad salarial en Colombia 1984-2005: cambios en la composición del mercado laboral y retornos a la educación post-secundaria," Borradores de Economia 5003, Banco de la Republica.
    13. Samantha Leorato & Franco Peracchi, 2015. "Comparing Distribution and Quantile Regression," EIEF Working Papers Series 1511, Einaudi Institute for Economics and Finance (EIEF), revised Oct 2015.
    14. Catania, Leopoldo & Luati, Alessandra, 2023. "Semiparametric modeling of multiple quantiles," Journal of Econometrics, Elsevier, vol. 237(2).
    15. Filip Žikeš & Jozef Baruník, 2016. "Semi-parametric Conditional Quantile Models for Financial Returns and Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 14(1), pages 185-226.
    16. David Kohns & Tibor Szendrei, 2021. "Decoupling Shrinkage and Selection for the Bayesian Quantile Regression," Papers 2107.08498, arXiv.org.
    17. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Some New Asymptotic Theory for Least Squares Series: Pointwise and Uniform Results," Papers 1212.0442, arXiv.org, revised Jun 2015.
    18. Knüppel, Malte & Krüger, Fabian & Pohle, Marc-Oliver, 2022. "Score-based calibration testing for multivariate forecast distributions," Discussion Papers 50/2022, Deutsche Bundesbank.
    19. Clément de Chaisemartin, 2012. "Fuzzy differences in differences," PSE Working Papers halshs-00671368, HAL.
    20. Habert white & Tae-Hwan Kim & Simone Manganelli, 2012. "VAR for VaR: Measuring Tail Dependence Using Multivariate Regression Quantiles," Working papers 2012rwp-45, Yonsei University, Yonsei Economics Research Institute.
    21. Artur J. Lemonte & Germán Moreno-Arenas, 2020. "On a heavy-tailed parametric quantile regression model for limited range response variables," Computational Statistics, Springer, vol. 35(1), pages 379-398, March.
    22. Xenxo Vidal-Llana & Carlos Salort Sánchez & Vincenzo Coia & Montserrat Guillen, 2022. ""Non-Crossing Dual Neural Network: Joint Value at Risk and Conditional Tail Expectation estimations with non-crossing conditions"," IREA Working Papers 202215, University of Barcelona, Research Institute of Applied Economics, revised Oct 2022.
    23. Kim, Young-Joo & Daly, Vincent, 2019. "The Education Gradient in Health: The Case of Obesity in the UK and US," Economics Discussion Papers 2019-4, School of Economics, Kingston University London.
    24. Fan, Yanqin & Liu, Ruixuan, 2016. "A direct approach to inference in nonparametric and semiparametric quantile models," Journal of Econometrics, Elsevier, vol. 191(1), pages 196-216.
    25. Sohag, Kazi & Kliestik, Tomas & Shams, S.M. Riad & Mariev, Oleg & Davidson, Natalia, 2022. "Capital market deepening, Governor’s characteristics and Russian regional enterprises: A big data analysis," Journal of Business Research, Elsevier, vol. 149(C), pages 340-352.
    26. Clément de Chaisemartin & Xavier d'Haultfoeuille, 2014. "Fuzzy Changes-in-Changes," Working Papers 2014-18, Center for Research in Economics and Statistics.
    27. Choonsung Park, 2020. "Consumption, Reservation Wages, and Aggregate Labor Supply," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 37, pages 54-80, July.
    28. Thierry Cohignac & Nabil Kazi-Tani, 2019. "Quantile Mixing and Model Uncertainty Measures," Working Papers hal-02405859, HAL.
    29. Gail Pacheco & Bill Cochrane, 2015. "Decomposing the temporary-permanent wage gap in New Zealand," Working Papers 2015-07, Auckland University of Technology, Department of Economics.
    30. Santiago Pereda Fernández, 2019. "Identification and estimation of triangular models with a binary treatment," Temi di discussione (Economic working papers) 1210, Bank of Italy, Economic Research and International Relations Area.
    31. Manuel Arellano & Richard Blundell & Stéphane Bonhomme & Jack Light, 2024. "Heterogeneity of consumption responses to income shocks in the presence of nonlinear persistence," Post-Print hal-04536563, HAL.
    32. Matthew Masten & Alexandre Poirier, 2017. "Inference on breakdown frontiers," CeMMAP working papers 20/17, Institute for Fiscal Studies.
    33. Victor Chernozhukov & Ivan Fernandez-Val & Siyi Luo, 2018. "Distribution regression with sample selection, with an application to wage decompositions in the UK," CeMMAP working papers CWP68/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    34. Ruhose, Jens & Parey, Matthias & Waldinger, Fabian & Netz, Nicolai, 2015. "The Selection of High-Skilled Migrants," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113148, Verein für Socialpolitik / German Economic Association.
    35. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    36. Qi Li & Juan Lin & Jeffrey S. Racine, 2012. "Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions," Department of Economics Working Papers 2012-10, McMaster University.
    37. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    38. Blaise Melly und Kaspar W thrich, 2016. "Local quantile treatment effects," Diskussionsschriften dp1605, Universitaet Bern, Departement Volkswirtschaft.
    39. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly, 2012. "Inference on counterfactual distributions," CeMMAP working papers 05/12, Institute for Fiscal Studies.
    40. Manuel Arellano & Stéphane Bonhomme, 2017. "Quantile Selection Models With an Application to Understanding Changes in Wage Inequality," Econometrica, Econometric Society, vol. 85, pages 1-28, January.
    41. Patrick A. Adams & Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2020. "Forecasting Macroeconomic Risks," Staff Reports 914, Federal Reserve Bank of New York.
    42. Jonathan Berrisch & Florian Ziel, 2023. "Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Prices," Papers 2303.10019, arXiv.org, revised Feb 2024.
    43. Xing Yan & Weizhong Zhang & Lin Ma & Wei Liu & Qi Wu, 2020. "Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning," Papers 2010.08263, arXiv.org.
    44. Michael Gurkov & Osnat Zohar, 2022. "Growth at Risk: Forecast Distribution of GDP Growth in Israel," Bank of Israel Working Papers 2022.08, Bank of Israel.
    45. Brantly Callaway, 2017. "Job Displacement during the Great Recession: Tight Bounds on Distributional Treatment Effect Parameters using Panel Data," DETU Working Papers 1703, Department of Economics, Temple University.
    46. Samantha Leorato & Franco Peracchi, 2015. "Shape Regressions," EIEF Working Papers Series 1506, Einaudi Institute for Economics and Finance (EIEF), revised Jul 2015.
    47. James Mitchell & Aubrey Poon & Dan Zhu, 2022. "Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics," Working Papers 22-12R, Federal Reserve Bank of Cleveland, revised 11 Apr 2023.
    48. Victor Chernozhukov & Ivan Fernández-Val & Sukjin Han & Amanda Kowalski, 2019. "Censored quantile instrumental-variable estimation with Stata," Stata Journal, StataCorp LP, vol. 19(4), pages 768-781, December.
    49. Julio Galvez & Javier Mencía, 2014. "Distributional Linkages between European Sovereign Bond and Bank Asset Returns," Working Papers wp2014_1407, CEMFI.
    50. Kazi Musa & Norli Ali & Jamaliah Said & Farha Ghapar & Oleg Mariev & Norhayati Mohamed & Hirnissa Mohd Tahir, 2023. "Does the Effectiveness of Budget Deficit Vary between Welfare and Non-Welfare Countries?," Sustainability, MDPI, vol. 15(5), pages 1-22, February.
    51. Li, Ting & Shi, Chengchun & Lu, Zhaohua & Li, Yi & Zhu, Hongtu, 2024. "Evaluating dynamic conditional quantile treatment effects with applications in ridesharing," LSE Research Online Documents on Economics 122488, London School of Economics and Political Science, LSE Library.
    52. Giovanni Bonaccolto & Massimiliano Caporin & Rangan Gupta, 2015. "The Dynamic Impact of Uncertainty in Causing and Forecasting the Distribution of Oil Returns and Risk," Working Papers 201564, University of Pretoria, Department of Economics.
    53. Markus Frolich & Blaise Melly, 2010. "Estimation of quantile treatment effects with Stata," Stata Journal, StataCorp LP, vol. 10(3), pages 423-457, September.
    54. He, Yaoyao & Zheng, Yaya, 2018. "Short-term power load probability density forecasting based on Yeo-Johnson transformation quantile regression and Gaussian kernel function," Energy, Elsevier, vol. 154(C), pages 143-156.
    55. Ivan Fernandez-Val & Wayne Yuan Gao & Yuan Liao & Francis Vella, 2022. "Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes," Papers 2202.04154, arXiv.org, revised Jan 2023.
    56. Roger Koenker & Samantha Leorato & Franco Peracchi, 2013. "Distributional vs. Quantile Regression," CEIS Research Paper 300, Tor Vergata University, CEIS, revised 17 Dec 2013.
    57. Ziel, Florian, 2022. "M5 competition uncertainty: Overdispersion, distributional forecasting, GAMLSS, and beyond," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1546-1554.
    58. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    59. Matthew A. Masten & Alexandre Poirier & Linqi Zhang, 2024. "Assessing Sensitivity to Unconfoundedness: Estimation and Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(1), pages 1-13, January.
    60. Ilaria Lucrezia Amerise, 2013. "Weighted Non-Crossing Quantile Regressions," Working Papers 201308, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    61. Siyi Wang & Xing Yan & Bangqi Zheng & Hu Wang & Wangli Xu & Nanbo Peng & Qi Wu, 2021. "Risk and return prediction for pricing portfolios of non-performing consumer credit," Papers 2110.15102, arXiv.org.
    62. Cannon, Alex J., 2017. "Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes," Earth Arxiv wg7sn, Center for Open Science.
    63. Gimenes, Nathalie & Guerre, Emmanuel, 2022. "Quantile regression methods for first-price auctions," Journal of Econometrics, Elsevier, vol. 226(2), pages 224-247.
    64. Mendolia, Silvia & Paloyo, Alfredo R. & Walker, Ian, 2016. "Heterogeneous effects of high school peers on educational outcomes," Ruhr Economic Papers 612, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    65. Tibor Szendrei & Arnab Bhattacharjee, 2024. "Momentum Informed Inflation-at-Risk," Papers 2408.12286, arXiv.org.
    66. Le Gal La Salle, Josselin & Badosa, Jordi & David, Mathieu & Pinson, Pierre & Lauret, Philippe, 2020. "Added-value of ensemble prediction system on the quality of solar irradiance probabilistic forecasts," Renewable Energy, Elsevier, vol. 162(C), pages 1321-1339.
    67. Alonso-Suárez, R. & David, M. & Branco, V. & Lauret, P., 2020. "Intra-day solar probabilistic forecasts including local short-term variability and satellite information," Renewable Energy, Elsevier, vol. 158(C), pages 554-573.
    68. Romauld Méango, 2023. "Identification of ex ante returns using elicited choice probabilities," Economics Series Working Papers 1007, University of Oxford, Department of Economics.
    69. Florent Dubois, 2017. "The Sources of Segregation," AMSE Working Papers 1720, Aix-Marseille School of Economics, France.
    70. Gabriel Montes-Rojas & Lucas Siga & Ram Mainali, 2017. "Mean and quantile regression Oaxaca-Blinder decompositions with an application to caste discrimination," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 15(3), pages 245-255, September.
    71. Iv'an Fern'andez-Val & Hugo Freeman & Martin Weidner, 2020. "Low-Rank Approximations of Nonseparable Panel Models," Papers 2010.12439, arXiv.org, revised Mar 2021.
    72. Frölich, Markus & Melly, Blaise, 2008. "Quantile Treatment Effects in the Regression Discontinuity Design," IZA Discussion Papers 3638, Institute of Labor Economics (IZA).
    73. Francisco Covas & Ben Rump & Egon Zakrajšek, 2013. "Stress-testing U.S. bank holding companies: a dynamic panel quantile regression approach," Finance and Economics Discussion Series 2013-55, Board of Governors of the Federal Reserve System (U.S.).
    74. Manuel Arellano & Stéphane Bonhomme, 2015. "Nonlinear panel data estimation via quantile regressions," CeMMAP working papers 40/15, Institute for Fiscal Studies.
    75. Ritter, Joseph A. & West, Kristine L., 2014. "Field of Study and Earnings Inequality among the Highly Educated: 1993-2010," Miscellaneous Publications 195683, University of Minnesota, Department of Applied Economics.
    76. Victor Chernozhukov & Ivan Fernandez-Val & Amanda Kowalski, 2011. "Quantile Regression with Censoring and Endogeneity," Cowles Foundation Discussion Papers 1797, Cowles Foundation for Research in Economics, Yale University.
    77. Zhongjun Qu & Jungmo Yoon, 2011. "Nonparametric Estimation and Inference on Conditional Quantile Processes," Boston University - Department of Economics - Working Papers Series WP2011-059, Boston University - Department of Economics.
    78. Maria Koumenta & Mario Pagliero & Davud Rostam-Afschar, 2020. "Occupational Licensing and the Gender Wage Gap," Working Papers 563, ECINEQ, Society for the Study of Economic Inequality.
    79. Leorato, Samantha & Peracchi, Franco & Tanase, Andrei V., 2012. "Asymptotically efficient estimation of the conditional expected shortfall," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 768-784.
    80. Holger Dette & Regine Scheder, 2011. "Estimation of additive quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(2), pages 245-265, April.
    81. Vu, Tien Manh & Yamada, Hiroyuki, 2017. "Decomposing Gender Equality along the Wage Distribution in Vietnam during the Period 2002–14," AGI Working Paper Series 2017-04, Asian Growth Research Institute.
    82. Lei, Heng & Xue, Minggao & Liu, Huiling, 2022. "Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors," Energy Economics, Elsevier, vol. 113(C).
    83. Fissler, Tobias & Merz, Michael & Wüthrich, Mario V., 2023. "Deep quantile and deep composite triplet regression," Insurance: Mathematics and Economics, Elsevier, vol. 109(C), pages 94-112.
    84. Karsten Schweikert, 2019. "Asymmetric price transmission in the US and German fuel markets: a quantile autoregression approach," Empirical Economics, Springer, vol. 56(3), pages 1071-1095, March.
    85. Tomasz Serafin & Bartosz Uniejewski & Rafal Weron, 2019. "Averaging predictive distributions across calibration windows for day-ahead electricity price forecasting," WORking papers in Management Science (WORMS) WORMS/19/08, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, revised 06 Jul 2019.
    86. Manuel Arellano & Richard Blundell & Stéphane Bonhomme, 2015. "Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework," Working Papers wp2015_1506, CEMFI.
    87. Angela Noufaily & M. C. Jones, 2013. "Parametric quantile regression based on the generalized gamma distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(5), pages 723-740, November.
    88. Ying-Ying Lee, 2015. "Interpretation and Semiparametric Efficiency in Quantile Regression under Misspecification," Econometrics, MDPI, vol. 4(1), pages 1-14, December.
    89. Wagner Piazza Gaglianone & Luiz Renato Lima, 2012. "Constructing Density Forecasts from Quantile Regressions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(8), pages 1589-1607, December.
    90. Tongseok Lim, 2023. "Replication of financial derivatives under extreme market models given marginals," Papers 2307.00807, arXiv.org.
    91. Carolina Castagnetti, 2015. "The Analysis of the Gender Wage Gap in the Italian Public Sector: a Quantile Approach for Panel Data," DEM Working Papers Series 109, University of Pavia, Department of Economics and Management.
    92. Lu, Shixiang & Xu, Qifa & Jiang, Cuixia & Liu, Yezheng & Kusiak, Andrew, 2022. "Probabilistic load forecasting with a non-crossing sparse-group Lasso-quantile regression deep neural network," Energy, Elsevier, vol. 242(C).
    93. David Kohns & Tibor Szendrei, 2020. "Horseshoe Prior Bayesian Quantile Regression," Papers 2006.07655, arXiv.org, revised Mar 2021.
    94. Kurisu, Daisuke & Otsu, Taisuke, 2023. "Subsampling inference for nonparametric extremal conditional quantiles," LSE Research Online Documents on Economics 120365, London School of Economics and Political Science, LSE Library.
    95. Lamarche, Carlos, 2011. "Measuring the incentives to learn in Colombia using new quantile regression approaches," Journal of Development Economics, Elsevier, vol. 96(2), pages 278-288, November.
    96. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2017. "Non-separable Models with High-dimensional Data," Economics and Statistics Working Papers 15-2017, Singapore Management University, School of Economics.
    97. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2020. "The Efficiency Gap," Papers 2010.14146, arXiv.org, revised Sep 2022.
    98. Zongwu Cai & Ying Fang & Ming Lin & Yaqian Wu, 2024. "Estimating Counterfactual Distribution Functions via Optimal Distribution Balancing with Applications," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202415, University of Kansas, Department of Economics.
    99. Meager, Rachael, 2022. "Aggregating distributional treatment effects: a Bayesian hierarchical analysis of the microcredit literature," LSE Research Online Documents on Economics 115559, London School of Economics and Political Science, LSE Library.
    100. Holger Dette & Matthias Guhlich & Natalie Neumeyer, 2015. "Testing for additivity in nonparametric quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(3), pages 437-477, June.
    101. Ghossoub, Mario, 2011. "Monotone equimeasurable rearrangements with non-additive probabilities," MPRA Paper 37629, University Library of Munich, Germany, revised 23 Mar 2012.
    102. Maike Hohberg & Peter Pütz & Thomas Kneib, 2020. "Treatment effects beyond the mean using distributional regression: Methods and guidance," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-29, February.
    103. Giuseppe Storti & Chao Wang, 2021. "Modelling uncertainty in financial tail risk: a forecast combination and weighted quantile approach," Papers 2104.04918, arXiv.org, revised Jul 2021.
    104. Liu, Nianqing & Vuong, Quang & Xu, Haiqing, 2017. "Rationalization and identification of binary games with correlated types," Journal of Econometrics, Elsevier, vol. 201(2), pages 249-268.
    105. Tomasz Serafin & Grzegorz Marcjasz & Rafal Weron, 2020. "Trading on short-term path forecasts of intraday electricity prices," WORking papers in Management Science (WORMS) WORMS/20/17, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    106. Gaglianone, Wagner Piazza & Marins, Jaqueline Terra Moura, 2017. "Evaluation of exchange rate point and density forecasts: An application to Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 707-728.
    107. Bryan S. Graham & Jinyong Hahn & Alexandre Poirier & James L. Powell, 2015. "Quantile Regression with Panel Data," NBER Working Papers 21034, National Bureau of Economic Research, Inc.
    108. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    109. Philippe Lauret & Mathieu David & Hugo T. C. Pedro, 2017. "Probabilistic Solar Forecasting Using Quantile Regression Models," Energies, MDPI, vol. 10(10), pages 1-17, October.
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    1. Nepp, Alexander & Okhrin, Ostap & Egorova, Julia & Dzhuraeva, Zarnigor & Zykov, Alexander, 2022. "What threatens stock markets more - The coronavirus or the hype around it?," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 519-539.
    2. Mehmet Caner & Esfandiar Maasoumi & Juan Andrés Riquelme, 2016. "Moment and IV Selection Approaches: A Comparative Simulation Study," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1562-1581, December.
    3. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2020. "Ill-posed estimation in high-dimensional models with instrumental variables," Journal of Econometrics, Elsevier, vol. 219(1), pages 171-200.
    4. Andrii Babii, 2020. "High-dimensional mixed-frequency IV regression," Papers 2003.13478, arXiv.org.
    5. Adel Javanmard & Jason D. Lee, 2020. "A flexible framework for hypothesis testing in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 685-718, July.
    6. Felipe González & Magdalena Larreboure, 2021. "The Impact of the Women’s March on the U.S. House Election," Documentos de Trabajo 560, Instituto de Economia. Pontificia Universidad Católica de Chile..
    7. Ellora Derenoncourt, 2022. "Can You Move to Opportunity? Evidence from the Great Migration," American Economic Review, American Economic Association, vol. 112(2), pages 369-408, February.
    8. Alena Skolkova, 2023. "Instrumental Variable Estimation with Many Instruments Using Elastic-Net IV," CERGE-EI Working Papers wp759, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    9. Belloni, Alexandre & Hansen, Christian & Newey, Whitney, 2022. "High-dimensional linear models with many endogenous variables," Journal of Econometrics, Elsevier, vol. 228(1), pages 4-26.

  125. Alexandre Belloni & Victor Chernozhukov, 2010. "Post-l1-penalized estimators in high-dimensional linear regression models," CeMMAP working papers CWP13/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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    1. Adam Nowak & Patrick Smith, 2015. "Textual Analysis in Real Estate," Working Papers 15-34, Department of Economics, West Virginia University.
    2. Heng Lian, 2012. "Variable selection in high-dimensional partly linear additive models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(4), pages 825-839, December.

  126. Alexandre Belloni & Daniel Chen & Victor Chernozhukov & Christian Hansen, 2010. "Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain," Papers 1010.4345, arXiv.org, revised Apr 2015.

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    2. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    3. Emily Cuddy & Janet Currie, 2020. "Rules vs. Discretion: Treatment of Mental Illness in U.S. Adolescents," Working Papers 2020-10, Princeton University. Economics Department..
    4. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    5. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
    6. Caner, Mehmet & Kock, Anders Bredahl, 2018. "Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso," Journal of Econometrics, Elsevier, vol. 203(1), pages 143-168.
    7. Breinlich, Holger & Corradi, Valentina & Rocha, Nadia & Ruta, Michele & Santos Silva, JMC & Zylkin, Thomas, 2022. "Machine Learning in International Trade Research - Evaluating the Impact of Trade Agreements," CEPR Discussion Papers 17325, C.E.P.R. Discussion Papers.
    8. Liqian Cai & Arnab Bhattacharjee & Roger Calantone & Taps Maiti, 2019. "Variable Selection with Spatially Autoregressive Errors: A Generalized Moments LASSO Estimator," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 146-200, September.
    9. Daniel Lewis & Davide Melcangi & Laura Pilossoph, 2024. "Latent Heterogeneity in the Marginal Propensity to Consume," NBER Working Papers 32523, National Bureau of Economic Research, Inc.
    10. Dennis Lim & Wenjie Wang & Yichong Zhang, 2022. "A Conditional Linear Combination Test with Many Weak Instruments," Papers 2207.11137, arXiv.org, revised Apr 2023.
    11. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023. "Granger Causality Testing in High-Dimensional VARs: A Post-Double-Selection Procedure," Journal of Financial Econometrics, Oxford University Press, vol. 21(3), pages 915-958.
    12. Robert S. Chirinko & Daniel J. Wilson, 2011. "Tax Competition Among U.S. States: Racing to the Bottom or Riding on a Seesaw?," CESifo Working Paper Series 3535, CESifo.
    13. Julián Caballero & Christian Upper, 2023. "What happens to EMEs when US yields go up?," BIS Working Papers 1081, Bank for International Settlements.
    14. Zhong, Wei & Gao, Yang & Zhou, Wei & Fan, Qingliang, 2021. "Endogenous treatment effect estimation using high-dimensional instruments and double selection," Statistics & Probability Letters, Elsevier, vol. 169(C).
    15. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, 2016. "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
    16. Neng-Chieh Chang, 2020. "The Mode Treatment Effect," Papers 2007.11606, arXiv.org.
    17. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    18. Linton, Oliver & Seo, Myung Hwan & Whang, Yoon-Jae, 2023. "Testing stochastic dominance with many conditioning variables," Journal of Econometrics, Elsevier, vol. 235(2), pages 507-527.
    19. Carlana, Michela & La Ferrara, Eliana, 2021. "Apart but Connected: Online Tutoring and Student Outcomes during the COVID-19 Pandemic," CEPR Discussion Papers 15761, C.E.P.R. Discussion Papers.
    20. Kaspar Wuthrich & Ying Zhu, 2019. "Omitted variable bias of Lasso-based inference methods: A finite sample analysis," Papers 1903.08704, arXiv.org, revised Sep 2021.
    21. Gyuhyeong Goh & Jisang Yu, 2022. "Causal inference with some invalid instrumental variables: A quasi‐Bayesian approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(6), pages 1432-1451, December.
    22. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2024. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 605-635.
    23. Chen, Daniel L. & Lind, Jo Thori, 2016. "The Political Economy of Beliefs: Why Fiscal and Social Conservatives/Liberals (Sometimes) Come Hand-in-Hand," TSE Working Papers 16-722, Toulouse School of Economics (TSE).
    24. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers 13/16, Institute for Fiscal Studies.
    25. Badawi, Adam B. & Chen, Daniel L., 2016. "The Shareholder Wealth Effects of Delaware Litigation," TSE Working Papers 16-683, Toulouse School of Economics (TSE).
    26. Anil Kumar, 2014. "Do restrictions on home equity extraction contribute to lower mortgage defaults? evidence from a policy discontinuity at the Texas border," Working Papers 1410, Federal Reserve Bank of Dallas.
    27. Rowena Gray & Greg C. Wright, 2022. "A Rising Tide? The Local Incidence of the Second Wave of Globalization," CESifo Working Paper Series 9725, CESifo.
    28. Vira Semenova & Matt Goldman & Victor Chernozhukov & Matt Taddy, 2023. "Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 471-510, May.
    29. Franz Huber & Alan Ponce & Francesco Rentocchini & Thomas Wainwright, 2020. "The Wealth of (Open Data) Nations? Examining the interplay of open government data and country-level institutions for entrepreneurial activity at the country-level," SEEDS Working Papers 1120, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Nov 2020.
    30. Hinnosaar, Marit & Liu, Elaine, 2020. "Persistence in alcohol consumption: evidence from migrants," CEPR Discussion Papers 15196, C.E.P.R. Discussion Papers.
    31. Bellmann, Lutz & Hübler, Olaf, 2019. "Personal Attitudes, Job Characteristics and Health," IZA Discussion Papers 12597, Institute of Labor Economics (IZA).
    32. Miao, Ke & Phillips, Peter C.B. & Su, Liangjun, 2023. "High-dimensional VARs with common factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 155-183.
    33. Santos, José Eusébio & Andini, Corrado, 2024. "Net privatisation prices in Brazil," Emerging Markets Review, Elsevier, vol. 60(C).
    34. Peter C. B. Phillips & Zhentao Shi, 2021. "Boosting: Why You Can Use The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 521-570, May.
    35. Eric Gautier & Christiern Rose, 2022. "Fast, Robust Inference for Linear Instrumental Variables Models using Self-Normalized Moments," Papers 2211.02249, arXiv.org, revised Nov 2022.
    36. Zhu, Ying, 2015. "Sparse Linear Models and l1−Regularized 2SLS with High-Dimensional Endogenous Regressors and Instruments," MPRA Paper 81217, University Library of Munich, Germany.
    37. Ivan A. Canay & Magne Mogstad & Jack Mountjoy, 2020. "On the Use of Outcome Tests for Detecting Bias in Decision Making," Working Papers 2020-125, Becker Friedman Institute for Research In Economics.
    38. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2024. "ddml: Double/debiased machine learning in Stata," Stata Journal, StataCorp LP, vol. 24(1), pages 3-45, March.
    39. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    40. Abhinandan Dalal & Patrick Blobaum & Shiva Kasiviswanathan & Aaditya Ramdas, 2024. "Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters," Papers 2408.09598, arXiv.org, revised Sep 2024.
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    42. Guangsu Zhou & Keyi Guo, 2023. "The impact of guardian's usage of the Internet on children's cognitive skills," International Studies of Economics, John Wiley & Sons, vol. 18(2), pages 193-210, June.
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    49. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
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    54. Julián Caballero, 2020. "Corporate dollar debt and depreciations: all's well that ends well?," BIS Working Papers 879, Bank for International Settlements.
    55. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers 62/13, Institute for Fiscal Studies.
    56. Jun Li & Serguei Netessine & Sergei Koulayev, 2018. "Price to Compete … with Many: How to Identify Price Competition in High-Dimensional Space," Management Science, INFORMS, vol. 64(9), pages 4118-4136, September.
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    63. Bilgin, Rumeysa, 2023. "The Selection Of Control Variables In Capital Structure Research With Machine Learning," SocArXiv e26qf, Center for Open Science.
    64. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    65. Chaohua Dong & Jiti Gao & Oliver Linton, 2018. "High dimensional semiparametric moment restriction models," CeMMAP working papers CWP04/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    70. Alexandre Belloni & Victor Chernozhukov, 2011. "High Dimensional Sparse Econometric Models: An Introduction," Papers 1106.5242, arXiv.org, revised Sep 2011.
    71. Alexandre Belloni & Victor Chernozhukov & Christian Hansen & Damian Kozbur, 2014. "Inference in High Dimensional Panel Models with an Application to Gun Control," Papers 1411.6507, arXiv.org.
    72. Brian Quistorff & Gentry Johnson, 2020. "Machine Learning for Experimental Design: Methods for Improved Blocking," Papers 2010.15966, arXiv.org.
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    75. Kanaya, Shin & Taylor, Luke, 2020. "Type I and Type II Error Probabilities in the Courtroom," MPRA Paper 100217, University Library of Munich, Germany.
    76. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
    77. Boris Fisera & Roman Horvath, 2020. "Are Exchange Rates Less Important for Trade in a More Globalized World? Evidence for the New EU Members," Working Papers IES 2020/10, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Apr 2020.
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    81. Bom, Judith & Bakx, Pieter & Schut, Frederik & van Doorslaer, Eddy, 2019. "Health effects of caring for and about parents and spouses," The Journal of the Economics of Ageing, Elsevier, vol. 14(C).
    82. Härdle, Wolfgang Karl & Chen, Shi & Liang, Chong & Schienle, Melanie, 2018. "Time-varying Limit Order Book Networks," IRTG 1792 Discussion Papers 2018-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
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    85. Nelson, Kelly & Brown, Zachary S. & Parton, Lee, 2019. "Biofuels Policy and Innovation Impacts: Evidence from Biofuels and Agricultural Patent Indicators," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 291243, Agricultural and Applied Economics Association.
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    89. Alessandro V. M. Oliveira & Bruno F. Oliveira & Moises D. Vassallo, 2024. "Airport service quality perception and flight delays: examining the influence of psychosituational latent traits of respondents in passenger satisfaction surveys," Papers 2401.02139, arXiv.org.
    90. Westphal, Matthias & Kamhöfer, Daniel A. & Schmitz, Hendrik, 2020. "Marginal College Wage Premiums under Selection into Employment," IZA Discussion Papers 13382, Institute of Labor Economics (IZA).
    91. Wunsch, Conny & Strittmatter, Anthony, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," CEPR Discussion Papers 15840, C.E.P.R. Discussion Papers.
    92. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    93. Shi, Zhentao, 2016. "Econometric estimation with high-dimensional moment equalities," Journal of Econometrics, Elsevier, vol. 195(1), pages 104-119.
    94. Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers 57/13, Institute for Fiscal Studies.
    95. Meijer, Erik & Spierdijk, Laura & Wansbeek, Tom, 2017. "Consistent estimation of linear panel data models with measurement error," Journal of Econometrics, Elsevier, vol. 200(2), pages 169-180.
    96. Barrera-Osorio, Felipe & Gertler,Paul J. & Nakajima,Nozomi & Patrinos,Harry Anthony, 2020. "Promoting Parental Involvement in Schools : Evidence from Two Randomized Experiments," Policy Research Working Paper Series 9462, The World Bank.
    97. Manudeep Bhuller & Gordon B. Dahl & Katrine V. Løken & Magne Mogstad, 2018. "Incarceration Spillovers in Criminal and Family Networks," NBER Working Papers 24878, National Bureau of Economic Research, Inc.
    98. Szabó-Morvai Ágnes & Hubert János Kiss, 2020. "Locus of control and Human Capital Investment Decisions: The Role of Effort, Parental Preferences and Financial Constraints," CERS-IE WORKING PAPERS 2055, Institute of Economics, Centre for Economic and Regional Studies.
    99. Sylvain Barde & Rowan Cherodian & Guy Tchuente, 2024. "Moran's I 2-Stage Lasso: for Models with Spatial Correlation and Endogenous Variables," Papers 2404.02584, arXiv.org.
    100. Colin F. Camerer & Gideon Nave & Alec Smith, 2019. "Dynamic Unstructured Bargaining with Private Information: Theory, Experiment, and Outcome Prediction via Machine Learning," Management Science, INFORMS, vol. 65(4), pages 1867-1890, April.
    101. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2016. "Post-Selection Inference for Generalized Linear Models With Many Controls," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 606-619, October.
    102. Aglasan, Serkan & Goodwin, Barry K. & Rejesus, Roderick, 2020. "Genetically Modified Rootworm-Resistant Corn, Risk, and Weather: Evidence from High Dimensional Methods," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 305181, Agricultural and Applied Economics Association.
    103. Santos, Luca J. & Oliveira, Alessandro V.M. & Aldrighi, Dante Mendes, 2021. "Testing the differentiated impact of the COVID-19 pandemic on air travel demand considering social inclusion," Journal of Air Transport Management, Elsevier, vol. 94(C).
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    106. Qingliang Fan & Yaqian Wu, 2020. "Endogenous Treatment Effect Estimation with some Invalid and Irrelevant Instruments," Papers 2006.14998, arXiv.org.
    107. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," Papers 1501.03185, arXiv.org.
    108. Ash, Elliott & Chen, Daniel L. & Lu, Wei, 2018. "Motivated Reasoning in the Field: Partisanship in Precedent, Prose, Vote, and Retirement in U.S. Circuit Courts, 1800-2013," TSE Working Papers 18-976, Toulouse School of Economics (TSE).
    109. Guy Tchuente, 2021. "A Note on the Topology of the First Stage of 2SLS with Many Instruments," Papers 2106.15003, arXiv.org.
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    112. Mehmet Caner & Anders Bredahl Kock, 2013. "Oracle Inequalities for Convex Loss Functions with Non-Linear Targets," CREATES Research Papers 2013-51, Department of Economics and Business Economics, Aarhus University.
    113. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio, 2017. "Economic Predictions with Big Data: The Illusion Of Sparsity," CEPR Discussion Papers 12256, C.E.P.R. Discussion Papers.
    114. Anders Bredahl Kock & Laurent A.F. Callot, 2012. "Oracle Inequalities for High Dimensional Vector Autoregressions," CREATES Research Papers 2012-16, Department of Economics and Business Economics, Aarhus University.
    115. Mehmet Caner & Xu Han & Yoonseok Lee, 2018. "Adaptive Elastic Net GMM Estimation With Many Invalid Moment Conditions: Simultaneous Model and Moment Selection," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 24-46, January.
    116. Peter C.B. Phillips & Zhentao Shi, 2019. "Boosting the Hodrick-Prescott Filter," Cowles Foundation Discussion Papers 2192, Cowles Foundation for Research in Economics, Yale University.
    117. Kolesár, Michal, 2018. "Minimum distance approach to inference with many instruments," Journal of Econometrics, Elsevier, vol. 204(1), pages 86-100.
    118. Marcelo C. Medeiros & Eduardo F. Mendes, 2012. "Estimating High-Dimensional Time Series Models," CREATES Research Papers 2012-37, Department of Economics and Business Economics, Aarhus University.
    119. Chen, Shi & Härdle, Wolfgang & Schienle, Melanie, 2021. "High-dimensional statistical learning techniques for time-varying limit order book networks," IRTG 1792 Discussion Papers 2021-015, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    120. Robert Brooks & Brandon N. Cline & Pavel Teterin & Yu You, 2022. "The information in global interest rate futures contracts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(6), pages 1135-1166, June.
    121. Helmut Wasserbacher & Martin Spindler, 2021. "Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls," Papers 2107.04851, arXiv.org.
    122. Stefan Seifert & Marica Valente, 2018. "An Offer that you Can't Refuse? Agrimafias and Migrant Labor on Vineyards in Southern Italy," Discussion Papers of DIW Berlin 1735, DIW Berlin, German Institute for Economic Research.
    123. Joshua Angrist, 2022. "Empirical Strategies in Economics: Illuminating the Path from Cause to Effect," NBER Working Papers 29726, National Bureau of Economic Research, Inc.
    124. Lonjezo Sithole, 2024. "A Locally Robust Semiparametric Approach to Examiner IV Designs," Papers 2404.19144, arXiv.org.
    125. Michael Danquah & Abdul Malik Iddrisu & Ernest Owusu Boakye & Solomon Owusu, 2021. "Do gender wage differences within households influence women's empowerment and welfare?: Evidence from Ghana," WIDER Working Paper Series wp-2021-40, World Institute for Development Economic Research (UNU-WIDER).
    126. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
    127. Mr. Andrew J Tiffin, 2019. "Machine Learning and Causality: The Impact of Financial Crises on Growth," IMF Working Papers 2019/228, International Monetary Fund.
    128. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2014. "Uniform post selection inference for LAD regression and other Z-estimation problems," CeMMAP working papers 51/14, Institute for Fiscal Studies.
    129. Kaila, Heidi & Azad, Abul, 2023. "The effects of crime and violence on food insecurity and consumption in Nigeria," Food Policy, Elsevier, vol. 115(C).
    130. Mitchell J. Lovett, 2019. "Empirical Research on Political Marketing: a Selected Review," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 6(3), pages 49-56, December.
    131. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
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    133. Chen, Daniel L. & Levonyan, Vardges & Yeh, Susan, 2016. "Policies Affect Preferences: Evidence from Random Variation in Abortion Jurisprudence," TSE Working Papers 16-723, Toulouse School of Economics (TSE).
    134. Brett R. Gordon & Florian Zettelmeyer & Neha Bhargava & Dan Chapsky, 2019. "A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook," Marketing Science, INFORMS, vol. 38(2), pages 193-225, March.
    135. Guilherme Lichand & Anandi Mani, 2020. "Cognitive Droughts," CSAE Working Paper Series 2020-02, Centre for the Study of African Economies, University of Oxford.
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    137. Sheldon, Tamara L. & DeShazo, J.R., 2017. "How does the presence of HOV lanes affect plug-in electric vehicle adoption in California? A generalized propensity score approach," Journal of Environmental Economics and Management, Elsevier, vol. 85(C), pages 146-170.
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    140. Halko, Marja-Liisa & Lappalainen, Olli & Sääksvuori, Lauri, 2021. "Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 87-104.
    141. Craig McIntosh & Andrew Zeitlin, 2020. "Using Household Grants to Benchmark the Cost Effectiveness of a USAID Workforce Readiness Program," Papers 2009.01749, arXiv.org.
    142. Jelena Bradic & Victor Chernozhukov & Whitney K. Newey & Yinchu Zhu, 2019. "Minimax Semiparametric Learning With Approximate Sparsity," Papers 1912.12213, arXiv.org, revised Aug 2022.
    143. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.
    144. Ruf, Daniel, 2017. "Agglomeration Effects and Liquidity Gradients in Local Rental Housing Markets," Working Papers on Finance 1702, University of St. Gallen, School of Finance.
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    146. Frank Windmeijer & Xiaoran Liang & Fernando P Hartwig & Jack Bowden, 2019. "The Confidence Interval Method for Selecting Valid Instrumental Variables," Bristol Economics Discussion Papers 19/715, School of Economics, University of Bristol, UK.
    147. Evan D. Peet & David Powell & Rosalie Liccardo Pacula, 2024. "Using Policy and Innovation to Improve Life-Saving Access to Naloxone," NBER Working Papers 33105, National Bureau of Economic Research, Inc.
    148. Kasey Buckles & Daniel Hungerman & Steven Lugauer, 2021. "Is Fertility a Leading Economic Indicator?," The Economic Journal, Royal Economic Society, vol. 131(634), pages 541-565.
    149. Li, Jing & Li, Liyao & Liu, Shimeng, 2022. "Attenuation of agglomeration economies: Evidence from the universe of Chinese manufacturing firms," Journal of Urban Economics, Elsevier, vol. 130(C).
    150. Mert Hakan Hekimoğlu & Burak Kazaz, 2020. "Analytics for Wine Futures: Realistic Prices," Production and Operations Management, Production and Operations Management Society, vol. 29(9), pages 2096-2120, September.
    151. Saulius Jokubaitis & Remigijus Leipus, 2022. "Asymptotic Normality in Linear Regression with Approximately Sparse Structure," Mathematics, MDPI, vol. 10(10), pages 1-28, May.
    152. Kilic, Gizem, 2021. "State-level Food Waste Policies In the U.S.: A Predictive Modelling," 2021 Annual Meeting, August 1-3, Austin, Texas 314091, Agricultural and Applied Economics Association.
    153. Isaac K. Ofori & Camara K. Obeng & Simplice A. Asongu, 2024. "What Really Drives Economic Growth in Sub-Saharan Africa? Evidence from the Lasso Regularization and Inferential Techniques," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 144-179, March.
    154. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    155. Harold D. Chiang, 2019. "Many Average Partial Effects: with an Application to Text Regression," 2019 Papers pch1836, Job Market Papers.
    156. Liran Einav & Jonathan D. Levin, 2013. "The Data Revolution and Economic Analysis," NBER Working Papers 19035, National Bureau of Economic Research, Inc.
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    158. Michal Kolesár & Raj Chetty & John N. Friedman & Edward L. Glaeser & Guido W. Imbens, 2011. "Identification and Inference with Many Invalid Instruments," NBER Working Papers 17519, National Bureau of Economic Research, Inc.
    159. Serkan Aglasan & Barry K. Goodwin & Roderick M. Rejesus, 2023. "Risk effects of GM corn: Evidence from crop insurance outcomes and high‐dimensional methods," Agricultural Economics, International Association of Agricultural Economists, vol. 54(1), pages 110-126, January.
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    161. Zhu, Ying, 2013. "Sparse Linear Models and Two-Stage Estimation in High-Dimensional Settings with Possibly Many Endogenous Regressors," MPRA Paper 49846, University Library of Munich, Germany.
    162. Robert Adamek & Stephan Smeekes & Ines Wilms, 2020. "Lasso Inference for High-Dimensional Time Series," Papers 2007.10952, arXiv.org, revised Sep 2022.
    163. Achim Ahrens, 2015. "Civil conflicts in Africa: Climate, economic shocks, nighttime lights and spill-over effects," SEEC Discussion Papers 1501, Spatial Economics and Econometrics Centre, Heriot Watt University.
    164. Fu Ouyang & Thomas Tao Yang, 2023. "High Dimensional Binary Choice Model with Unknown Heteroskedasticity or Instrumental Variables," Papers 2311.07067, arXiv.org.
    165. Berden, Carolien & Croes, R. & Kemp, R. & Mikkers, Misja & van der Noll, Rob & Shestalova, V. & Svitak, Jan, 2019. "Hospital Competition in the Netherlands : An Empirical Investigation," Other publications TiSEM 5302ab6a-9099-4b5d-9874-8, Tilburg University, School of Economics and Management.
    166. Marc Arnold & Dustin Schuette & Alexander Wagner, 2021. "Neglected Risk in Financial Innovation: Evidence from Structured Product Counterparty Exposure," European Financial Management, European Financial Management Association, vol. 27(2), pages 287-325, March.
    167. Marianne Bl'ehaut & Xavier D'Haultfoeuille & J'er'emy L'Hour & Alexandre B. Tsybakov, 2020. "An alternative to synthetic control for models with many covariates under sparsity," Papers 2005.12225, arXiv.org, revised Jun 2021.
    168. Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
    169. Pieter Bakx & Bram Wouterse & Eddy (E.K.A.) van Doorslaer & Albert Wong, 2018. "Better off at home? Effects of a nursing home admission on costs, hospitalizations and survival," Tinbergen Institute Discussion Papers 18-060/V, Tinbergen Institute.
    170. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2017. "Non-separable Models with High-dimensional Data," Economics and Statistics Working Papers 15-2017, Singapore Management University, School of Economics.
    171. Eugene Dettaa & Endong Wang, 2024. "Inference in High-Dimensional Linear Projections: Multi-Horizon Granger Causality and Network Connectedness," Papers 2410.04330, arXiv.org.
    172. Prosper Dovonon & Firmin Doko Tchatoka & Michael Aguessy, 2019. "Relevant moment selection under mixed identification strength," School of Economics and Public Policy Working Papers 2019-04, University of Adelaide, School of Economics and Public Policy.
    173. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2020. "Ill-posed estimation in high-dimensional models with instrumental variables," Journal of Econometrics, Elsevier, vol. 219(1), pages 171-200.
    174. Olaf Hübler, 2023. "Donations, volunteering, and life satisfaction in Germany," Economics Bulletin, AccessEcon, vol. 43(4), pages 1908-1927.
    175. Victor Chernozhukov & Ivan Fernandez-Val & Chen Huang & Weining Wang, 2024. "Arellano-bond lasso estimator for dynamic linear panel models," CeMMAP working papers 09/24, Institute for Fiscal Studies.
    176. Yan Sun & Wei Huang, 2024. "Estimation and testing of kink regression model with endogenous regressors," Computational Statistics, Springer, vol. 39(6), pages 3115-3135, September.
    177. Stelios Michalopoulos & Melanie Meng Xue, 2021. "Folklore," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(4), pages 1993-2046.
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    179. Undral Byambadalai, 2022. "Identification and Inference for Welfare Gains without Unconfoundedness," Papers 2207.04314, arXiv.org.
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    232. Orlando Sotomayor, 2019. "Growth with reduction in poverty and inequality: did Brazil show the way?," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 17(4), pages 521-541, December.
    233. Romuald Foueka, 2020. "Analyse du différentiel de performances scolaires dans les pays PASEC sur la base de la régression quantile contrefactuelle," African Development Review, African Development Bank, vol. 32(4), pages 605-618, December.
    234. Pallab Kumar Ghosh & Jae Yoon Lee, 2016. "Decomposition of Changes in Korean Wage Inequality, 1998–2007," Journal of Labor Research, Springer, vol. 37(1), pages 1-28, March.
    235. Idrisov, Georgiy (Идрисов, Георгий) & Taganov, B.V. (Таганов, Б.), 2016. "Research of the Effect of Growth of Openness of the Russian Economy on Income Inequality in Russia [Исследование Влияния Роста Открытости Российской Экономики На Неравенство Доходов Населения В Рос," Working Papers 3136, Russian Presidential Academy of National Economy and Public Administration.
    236. Chung Choe & Philippe Van Kerm, 2018. "Foreign Workers and the Wage Distribution: What Does the Influence Function Reveal?," Econometrics, MDPI, vol. 6(3), pages 1-26, September.
    237. Bargain, Olivier & Etienne, Audrey & Melly, Blaise, 2021. "Informal pay gaps in good and bad times: Evidence from Russia," Journal of Comparative Economics, Elsevier, vol. 49(3), pages 693-714.
    238. Smirnykh, L. & Polaykova, E., 2020. "Income and the integration of migrants in the Russian labour market," Journal of the New Economic Association, New Economic Association, vol. 47(3), pages 84-104.
    239. Pérez Pérez Jorge, 2019. "Minimum Wages in Formal and Informal Sectors: Evidence from an Inflation Shock in Colombia," Working Papers 2019-13, Banco de México.
    240. Christian S. Otchia, 2019. "On Promoting Entrepreneurship and Job Creation in Africa: Evidence from Ghana and Kenya," Economics Bulletin, AccessEcon, vol. 39(2), pages 908-918.
    241. Alexander Silbersdorff & Julia Lynch & Stephan Klasen & Thomas Kneib, 2017. "Reconsidering the Income-Illness Relationship Using Distributional Regression: An Application to Germany," SOEPpapers on Multidisciplinary Panel Data Research 931, DIW Berlin, The German Socio-Economic Panel (SOEP).
    242. Priscillia Hunt, 2012. "From the bottom to the top: a more complete picture of the immigrant-native wage gap in Britain," IZA Journal of Migration and Development, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 1(1), pages 1-18, December.
    243. Wolski, Marcin, 2018. "Sovereign risk and corporate cost of borrowing: Evidence from a counterfactual study," EIB Working Papers 2018/05, European Investment Bank (EIB).
    244. BARGAIN Olivier & DOORLEY Karina & VAN KERM Philippe, 2016. "Minimum wages and the gender gap in pay. Evidence from the UK and Ireland," LISER Working Paper Series 2016-02, Luxembourg Institute of Socio-Economic Research (LISER).
    245. Brantly Callaway & Weige Huang, 2018. "Intergenerational Income Mobility: Counterfactual Distributions with a Continuous Treatment," DETU Working Papers 1801, Department of Economics, Temple University.
    246. Rahul Singh & Liyuan Xu & Arthur Gretton, 2021. "Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves," Papers 2111.03950, arXiv.org, revised Jul 2023.
    247. Bera Anil K. & Galvao Antonio F. & Montes-Rojas Gabriel V. & Park Sung Y., 2016. "Asymmetric Laplace Regression: Maximum Likelihood, Maximum Entropy and Quantile Regression," Journal of Econometric Methods, De Gruyter, vol. 5(1), pages 79-101, January.
    248. Ömer Limanlı, 2015. "Gender Based Wage Gap in Turkey," EY International Congress on Economics II (EYC2015), November 5-6, 2015, Ankara, Turkey 22, Ekonomik Yaklasim Association.
    249. Kampon Adireksombat & Zheng Fang & Chris Sakellariou, 2016. "The Evolution Of Gender Wage Differentials In Thailand: 1991–2007 — An Application Of Unconditional Quantile Regression," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 61(05), pages 1-30, December.
    250. Richard Spady & Sami Stouli, 2020. "Gaussian Transforms Modeling and the Estimation of Distributional Regression Functions," Papers 2011.06416, arXiv.org.
    251. Khalid Abu-Ismail & Verena Gantner & Paul Makdissi & Myra Yazbeck, 2020. "Socioeconomic inequalities in child malnutrition in Egypt," METRON, Springer;Sapienza Università di Roma, vol. 78(2), pages 175-191, August.
    252. Paul Allanson & Richard Cookson, 2022. "Comparing healthcare quality: A common framework for both ordinal and cardinal data with an application to primary care variation in England," Health Economics, John Wiley & Sons, Ltd., vol. 31(12), pages 2593-2608, December.
    253. Strittmatter, Anthony, 2019. "Heterogeneous earnings effects of the job corps by gender: A translated quantile approach," Labour Economics, Elsevier, vol. 61(C).
    254. Alexander Sohn, 2015. "Beyond Conventional Wage Discrimination Analysis: Assessing Comprehensive Wage Distributions of Males and Females Using Structured Additive Distributional Regression," SOEPpapers on Multidisciplinary Panel Data Research 802, DIW Berlin, The German Socio-Economic Panel (SOEP).
    255. Cho, Joonmo & Lee, Tai & Jung, Hanna, 2014. "Glass ceiling in a stratified labor market: Evidence from Korea," Journal of the Japanese and International Economies, Elsevier, vol. 32(C), pages 56-70.
    256. Debra Shepherd, 2013. "A question of efficiency: decomposing South African reading test scores using PIRLS 2006," Working Papers 20/2013, Stellenbosch University, Department of Economics.
    257. Victor Chernozhukov & Roberto Rigobon & Thomas M. Stoker, 2010. "Set identification and sensitivity analysis with Tobin regressors," Quantitative Economics, Econometric Society, vol. 1(2), pages 255-277, November.
    258. Carl Lin & Tony Fang & Mei Hsu, 2024. "Migrants from a Different Shore: Earnings and Economic Assimilation of Immigrants from China in the United States," Journal of Labor Research, Springer, vol. 45(3), pages 305-349, September.
    259. Rahul Singh, 2021. "Generalized Kernel Ridge Regression for Causal Inference with Missing-at-Random Sample Selection," Papers 2111.05277, arXiv.org.
    260. Yuanhua Feng & Wolfgang Karl Härdle, 2021. "Uni- and multivariate extensions of the sinh-arcsinh normal distribution applied to distributional regression," Working Papers CIE 142, Paderborn University, CIE Center for International Economics.
    261. Juan Carlos Escanciano & Chuan Goh, 2010. "Specification Analysis of Structural Quantile Regression Models," Working Papers tecipa-415, University of Toronto, Department of Economics.
    262. Sokbae Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2022. "Fast Inference for Quantile Regression with Tens of Millions of Observations," Papers 2209.14502, arXiv.org, revised Oct 2023.
    263. Fan, Yanqin & Shi, Xuetao & Tao, Jing, 2023. "Partial identification and inference in moment models with incomplete data," Journal of Econometrics, Elsevier, vol. 235(2), pages 418-443.
    264. Qin, Yu & Zhu, Hongjia & Zhu, Rong, 2016. "Changes in the distribution of land prices in urban China during 2007–2012," Regional Science and Urban Economics, Elsevier, vol. 57(C), pages 77-90.
    265. Luca Bonacini & Giovanni Gallo & Sergio Scicchitano, 2021. "Working from home and income inequality: risks of a ‘new normal’ with COVID-19," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 303-360, January.
    266. CHOE Chung & VAN KERM Philippe, 2014. "Foreign workers and the wage distribution: Where do they fit in?," LISER Working Paper Series 2014-02, Luxembourg Institute of Socio-Economic Research (LISER).
    267. Sander Barendse, 2017. "Interquantile Expectation Regression," Tinbergen Institute Discussion Papers 17-034/III, Tinbergen Institute.
    268. Nigel Rice & Maria Jose Aragon, 2018. "The determinants of health care expenditure growth," Working Papers 156cherp, Centre for Health Economics, University of York.
    269. David M. Kaplan, 2020. "Assessing Policy Effects with Unconditional Quantile Regression," Working Papers 2011, Department of Economics, University of Missouri.
    270. Delprato, Marcos & Chudgar, Amita, 2018. "Factors associated with private-public school performance: Analysis of TALIS-PISA link data," International Journal of Educational Development, Elsevier, vol. 61(C), pages 155-172.
    271. Nicolas Albacete & Pirmin Fessler & Martin Schürz, 2012. "Risk Buffer Profiles of Foreign Currency Mortgage Holders," Financial Stability Report, Oesterreichische Nationalbank (Austrian Central Bank), issue 23, pages 58-71.
    272. Tromp, Nikolas, 2019. "The narrowing gender wage gap in South Korea," Journal of the Japanese and International Economies, Elsevier, vol. 53(C), pages 1-1.
    273. Gibson, Grant & Clair, Luc, 2019. "O brother how art thou: Propensity to report self-assessed unmet need," Social Science & Medicine, Elsevier, vol. 243(C).
    274. Doan, Tinh & Ha, Van & Tran, Tuyen & Yang, Judy, 2023. "Dynamics of wage inequality over the prolonged economic transformation: The case of Vietnam," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 816-834.
    275. Santiago Pereda Fernández, 2016. "Estimation of counterfactual distributions with a continuous endogenous treatment," Temi di discussione (Economic working papers) 1053, Bank of Italy, Economic Research and International Relations Area.
    276. Rothe, Christoph & Wied, Dominik, 2020. "Estimating derivatives of function-valued parameters in a class of moment condition models," Journal of Econometrics, Elsevier, vol. 217(1), pages 1-19.
    277. SIERMINSKA Eva & DOORLEY Karina, 2012. "Decomposing household wealth portfolios across countries: An age-old question?," LISER Working Paper Series 2012-32, Luxembourg Institute of Socio-Economic Research (LISER).
    278. Dossche, Maarten & Kolndrekaj, Aleksandra & Propst, Maximilian & Ramos Perez, Javier & Slacalek, Jiri, 2022. "Immigrants and the distribution of income and wealth in the euro area: first facts and implications for monetary policy," Working Paper Series 2719, European Central Bank.
    279. Jessica Nisén & Johanna Tassot & Francesco Iacoella & Peter Eibich, 2022. "The effect of fertility timing on women’s earnings at midlife in the UK," MPIDR Working Papers WP-2022-021, Max Planck Institute for Demographic Research, Rostock, Germany.

  128. Victor Chernozhukov & Ivan Fernandez-Val & Jinyong Hahn & Whitney Newey, 2009. "Identification and Estimation of Marginal Effects in Nonlinear Panel Models," Boston University - Department of Economics - Working Papers Series wp2009-b, Boston University - Department of Economics.

    Cited by:

    1. Martin Browning & Jesus M. Carro, 2009. "Dynamic binary outcome models with maximal heterogeneity," Economics Series Working Papers 426, University of Oxford, Department of Economics.
    2. Andrei Markevich & Ekaterina Zhuravskaya, 2009. "Career Concerns in a Political Hierarchy: A Case of Regional Leaders in Soviet Russia," Working Papers w0040, Center for Economic and Financial Research (CEFIR).
    3. Ali Fakih, 2014. "Vacation Leave, Work Hours and Wages: New Evidence from Linked Employer-Employee Data," CIRANO Working Papers 2014s-40, CIRANO.
    4. Hoderlein, Stefan & White, Halbert, 2012. "Nonparametric identification in nonseparable panel data models with generalized fixed effects," Journal of Econometrics, Elsevier, vol. 168(2), pages 300-314.
    5. Khan, Shakeeb & Ponomareva, Maria & Tamer, Elie, 2011. "Identification of Panel Data Models with Endogenous Censoring," MPRA Paper 30373, University Library of Munich, Germany.
    6. Adam Rosen, 2009. "Set identification via quantile restrictions in short panels," CeMMAP working papers CWP26/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Philip Kostov & Julie Le Gallo, 2015. "Convergence: A Story of Quantiles and Spillovers," Kyklos, Wiley Blackwell, vol. 68(4), pages 552-576, November.
    8. Markevich, Andrei & Zhuravskaya, Ekaterina, 2011. "M-form hierarchy with poorly-diversified divisions: A case of Khrushchev's reform in Soviet Russia," Journal of Public Economics, Elsevier, vol. 95(11), pages 1550-1560.
    9. Ji-Liang Shiu & Yingyao Hu, 2010. "Identification and Estimation of Nonlinear Dynamic Panel Data Models with Unobserved Covariates," Economics Working Paper Archive 557, The Johns Hopkins University,Department of Economics.
    10. Manuel Arellano & Stéphane Bonhomme, 2009. "Identifying distributional characteristics in random coefficients panel data models," CeMMAP working papers CWP22/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Ciani, Emanuele, 2012. "Informal adult care and caregivers' employment in Europe," Labour Economics, Elsevier, vol. 19(2), pages 155-164.
    12. Bryan S. Graham & James Powell, 2008. "Identification and Estimation of 'Irregular' Correlated Random Coefficient Models," NBER Working Papers 14469, National Bureau of Economic Research, Inc.
    13. Hyland, Marie & Bertsch, Valentin, 2018. "The Role of Community Involvement Mechanisms in Reducing Resistance to Energy Infrastructure Development," Ecological Economics, Elsevier, vol. 146(C), pages 447-474.
    14. Kyungchul Song, 2009. "Point Decisions for Interval-Identified Parameters," PIER Working Paper Archive 09-036, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    15. Bertsch, Valentin & Hyland, Marie & Mahony, Michael, 2017. "What drives people's opinions of electricity infrastructure? Empirical evidence from Ireland," Energy Policy, Elsevier, vol. 106(C), pages 472-497.
    16. Amaresh Tiwari & Franz Palm, 2011. "Nonlinear Panel Data Models with Expected a Posteriori Values of Correlated Random Effects," CREPP Working Papers 1113, Centre de Recherche en Economie Publique et de la Population (CREPP) (Research Center on Public and Population Economics) HEC-Management School, University of Liège.
    17. Lewbel, Arthur & Yang, Thomas Tao, 2016. "Identifying the average treatment effect in ordered treatment models without unconfoundedness," Journal of Econometrics, Elsevier, vol. 195(1), pages 1-22.
    18. Bester, C. Alan & Hansen, Christian B., 2016. "Grouped effects estimators in fixed effects models," Journal of Econometrics, Elsevier, vol. 190(1), pages 197-208.
    19. Hu, Yingyao, 2017. "The Econometrics of Unobservables -- Latent Variable and Measurement Error Models and Their Applications in Empirical Industrial Organization and Labor Economics [The Econometrics of Unobservables]," Economics Working Paper Archive 64578, The Johns Hopkins University,Department of Economics, revised 2021.

  129. Victor Chernozhukov & Sokbae (Simon) Lee & Adam Rosen, 2009. "Intersection Bounds: estimation and inference," CeMMAP working papers 19/09, Institute for Fiscal Studies.

    Cited by:

    1. Ariel Pakes & Jack Porter, 2024. "Moment inequalities for multinomial choice with fixed effects," Quantitative Economics, Econometric Society, vol. 15(1), pages 1-25, January.
    2. Hiroaki Kaido, 2014. "Asymptotically efficient estimation of weighted average derivatives with an interval censored variable," CeMMAP working papers 03/14, Institute for Fiscal Studies.
    3. Brantly Callaway, 2020. "Bounds on Distributional Treatment Effect Parameters using Panel Data with an Application on Job Displacement," Papers 2008.08117, arXiv.org.
    4. Sokbae Lee & Ryo Okui & Yoon†Jae Whang, 2017. "Doubly robust uniform confidence band for the conditional average treatment effect function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1207-1225, November.
    5. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "On the asymptotic theory for least squares series: pointwise and uniform results," CeMMAP working papers 73/13, Institute for Fiscal Studies.
    6. Andres Aradillas-Lopez & Adam Rosen, 2013. "Inference in ordered response games with complete information," CeMMAP working papers 33/13, Institute for Fiscal Studies.
    7. Christian Bontemps & Thierry Magnac & Eric Maurin, 2012. "Set Identified Linear Models," Econometrica, Econometric Society, vol. 80(3), pages 1129-1155, May.
    8. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Testing many moment inequalities," CeMMAP working papers CWP42/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Peter Eibich & Thomas Siedler, 2020. "Retirement, Intergenerational Time Transfers, and Fertility," SOEPpapers on Multidisciplinary Panel Data Research 1073, DIW Berlin, The German Socio-Economic Panel (SOEP).
    10. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    11. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    12. Sam Cosaert & Thomas Demuynck, 2018. "Nonparametric welfare and demand analysis with unobserved individual heterogeneity," ULB Institutional Repository 2013/251988, ULB -- Universite Libre de Bruxelles.
    13. Yoichi Arai & Yu-Chin Hsu & Toru Kitagawa & Ismael Mourifié & Yuanyuan Wan, 2021. "Testing identifying assumptions in fuzzy regression discontinuity designs," CeMMAP working papers CWP16/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Kate Ho & Adam M. Rosen, 2015. "Partial Identification in Applied Research: Benefits and Challenges," NBER Working Papers 21641, National Bureau of Economic Research, Inc.
    15. Federico A. Bugni & Ivan A. Canay & Xiaoxia Shi, 2013. "Specification tests for partially identified models defined by moment inequalities," CeMMAP working papers CWP01/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Ismael Mourifie & Yuanyuan Wan, 2014. "Testing Local Average Treatment Effect Assumptions," Working Papers tecipa-514, University of Toronto, Department of Economics.
    17. Zheng Fang & Juwon Seo, 2021. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Econometrica, Econometric Society, vol. 89(5), pages 2439-2458, September.
    18. Thomas Carr & Toru Kitagawa, 2021. "Testing Instrument Validity with Covariates," Papers 2112.08092, arXiv.org, revised Sep 2023.
    19. Steven T Berry & Giovanni Compiani, 2023. "An Instrumental Variable Approach to Dynamic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(4), pages 1724-1758.
    20. Wooyoung Kim & Koohyun Kwon & Soonwoo Kwon & Sokbae Lee, 2018. "The identification power of smoothness assumptions in models with counterfactual outcomes," Quantitative Economics, Econometric Society, vol. 9(2), pages 617-642, July.
    21. Lafférs, Lukáš & Mellace, Giovanni, 2020. "Identification of the average treatment effect when SUTVA is violated," Discussion Papers on Economics 3/2020, University of Southern Denmark, Department of Economics.
    22. Laffers, Lukas, 2013. "Identification in Models with Discrete Variables," Discussion Paper Series in Economics 1/2013, Norwegian School of Economics, Department of Economics.
    23. Victor Chernozhukov & Ivan Fernandez-Val & Stefan Hoderlein & Hajo Holzmann & Whitney K. Newey, 2013. "Nonparametric identification in panels using quantiles," CeMMAP working papers 66/13, Institute for Fiscal Studies.
    24. Federico A. Bugni & Ivan A. Canay & Xiaoxia Shi, 2014. "Inference for functions of partially identified parameters in moment inequality models," CeMMAP working papers CWP05/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    25. Le-Yu Chen & Sokbae (Simon) Lee, 2017. "Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models," CeMMAP working papers CWP51/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    26. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Some New Asymptotic Theory for Least Squares Series: Pointwise and Uniform Results," Papers 1212.0442, arXiv.org, revised Jun 2015.
    27. Sokbae (Simon) Lee & Kyungchui (Kevin) Song & Yoon-Jae Whang, 2014. "Testing for a general class of functional inequalities," CeMMAP working papers CWP09/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    28. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Anti-concentration and honest, adaptive confidence bands," CeMMAP working papers 43/16, Institute for Fiscal Studies.
    29. Clément de Chaisemartin, 2012. "Fuzzy differences in differences," PSE Working Papers halshs-00671368, HAL.
    30. Santiago Acerenza & Otávio Bartalotti & Désiré Kédagni, 2023. "Testing identifying assumptions in bivariate probit models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 407-422, April.
    31. Gu, Jiaying & Russell, Thomas M., 2023. "Partial identification in nonseparable binary response models with endogenous regressors," Journal of Econometrics, Elsevier, vol. 235(2), pages 528-562.
    32. Gobillon, Laurent & Magnac, Thierry, 2013. "Regional Policy Evaluation:Interactive Fixed Effects and Synthetic Controls," TSE Working Papers 13-419, Toulouse School of Economics (TSE).
    33. Armstrong, Timothy B., 2014. "Weighted KS statistics for inference on conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 181(2), pages 92-116.
    34. Victor Chernozhukov & Sokbae Lee & Adam M. Rosen, 2013. "Intersection Bounds: Estimation and Inference," Econometrica, Econometric Society, vol. 81(2), pages 667-737, March.
    35. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Gaussian approximation of suprema of empirical processes," CeMMAP working papers CWP75/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    36. Vira Semenova, 2023. "Aggregated Intersection Bounds and Aggregated Minimax Values," Papers 2303.00982, arXiv.org, revised Jun 2024.
    37. Joel L. Horowitz & Sokbae (Simon) Lee, 2015. "Nonparametric estimation and inference under shape restrictions," CeMMAP working papers CWP67/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    38. Matthew Masten & Alexandre Poirier, 2017. "Inference on breakdown frontiers," CeMMAP working papers 20/17, Institute for Fiscal Studies.
    39. Khan, Shakeeb & Ponomareva, Maria & Tamer, Elie, 2011. "Identification of Panel Data Models with Endogenous Censoring," MPRA Paper 30373, University Library of Munich, Germany.
    40. D'Haultfoeuille, Xavier & Gaillac, Christophe & Maurel, Arnaud, 2022. "Partially Linear Models under Data Combination," IZA Discussion Papers 15230, Institute of Labor Economics (IZA).
    41. Chalak, Karim & Kim, Daniel & Miller, Megan & Pepper, John, 2022. "Reexamining the evidence on gun ownership and homicide using proxy measures of ownership," Journal of Public Economics, Elsevier, vol. 208(C).
    42. Blaise Melly und Kaspar W thrich, 2016. "Local quantile treatment effects," Diskussionsschriften dp1605, Universitaet Bern, Departement Volkswirtschaft.
    43. Han, Sukjin & Yang, Shenshen, 2024. "A computational approach to identification of treatment effects for policy evaluation," Journal of Econometrics, Elsevier, vol. 240(1).
    44. Jesus Gonzalo & Jose Olmo, 2014. "Conditional Stochastic Dominance Tests In Dynamic Settings," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(3), pages 819-838, August.
    45. Sung Jae Jun & Sokbae Lee, 2020. "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," Papers 2004.08318, arXiv.org, revised Oct 2023.
    46. Chesher, Andrew, 2013. "Semiparametric Structural Models Of Binary Response: Shape Restrictions And Partial Identification," Econometric Theory, Cambridge University Press, vol. 29(2), pages 231-266, April.
    47. D’Haultfœuille, Xavier & Maurel, Arnaud & Zhang, Yichong, 2018. "Extremal quantile regressions for selection models and the black–white wage gap," Journal of Econometrics, Elsevier, vol. 203(1), pages 129-142.
    48. Song, Song & Ritov, Ya’acov & Härdle, Wolfgang K., 2012. "Bootstrap confidence bands and partial linear quantile regression," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 244-262.
    49. Adam Rosen, 2009. "Set identification via quantile restrictions in short panels," CeMMAP working papers CWP26/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    50. Andrew Chesher & Adam Rosen, 2016. "Characterizations of identified sets delivered by structural econometric models," CeMMAP working papers CWP44/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    51. Jiannan Lu & Peng Ding & Tirthankar Dasgupta, 2018. "Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Bounds," Journal of Educational and Behavioral Statistics, , vol. 43(5), pages 540-567, October.
    52. Germinario, Giuseppe & Amin, Vikesh & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2022. "What can we learn about the effect of mental health on labor market outcomes under weak assumptions? Evidence from the NLSY79," Labour Economics, Elsevier, vol. 79(C).
    53. Giustinelli, Pamela & Manski, Charles F. & Molinari, Francesca, 2022. "Tail and center rounding of probabilistic expectations in the Health and Retirement Study," Journal of Econometrics, Elsevier, vol. 231(1), pages 265-281.
    54. Timothy B. Armstrong & Michal Koles'ar, 2017. "Finite-Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness," Papers 1712.04594, arXiv.org, revised Jan 2021.
    55. Andrew Chesher & Adam M. Rosen, 2021. "Counterfactual Worlds," Annals of Economics and Statistics, GENES, issue 142, pages 311-335.
    56. Chalak, Karim & Kim, Daniel, 2020. "Measurement error in multiple equations: Tobin’s q and corporate investment, saving, and debt," Journal of Econometrics, Elsevier, vol. 214(2), pages 413-432.
    57. Orville Mondal & Rui Wang, 2024. "Partial Identification of Binary Choice Models with Misreported Outcomes," Papers 2401.17137, arXiv.org.
    58. Alfonso Flores-Lagunes & Amin Vikesh & Carlos A. Flores, 2019. "The Impact of BMI on Mental Health: Further Evidence from Genetic Markers," Center for Policy Research Working Papers 216, Center for Policy Research, Maxwell School, Syracuse University.
    59. Isaiah Andrews & Toru Kitagawa & Adam McCloskey, 2018. "Inference on winners," CeMMAP working papers CWP73/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    60. Aviv Nevo & Adam Rosen, 2008. "Identification with imperfect instruments," CeMMAP working papers CWP16/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    61. Timothy B. Armstrong, 2014. "On the Choice of Test Statistic for Conditional Moment Inequalities," Cowles Foundation Discussion Papers 1960, Cowles Foundation for Research in Economics, Yale University.
    62. Sokbae Lee & Yoon-Jae Whang, 2009. "Nonparametric Tests of Conditional Treatment Effects," Cowles Foundation Discussion Papers 1740, Cowles Foundation for Research in Economics, Yale University.
    63. de Chaisemartin, Clement & D'Haultfoeuille, Xavier, "undated". "Supplement to Fuzzy Differences-in-Differences," Economic Research Papers 270217, University of Warwick - Department of Economics.
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    217. Bei, Xinyue, 2024. "Local linearization based subvector inference in moment inequality models," Journal of Econometrics, Elsevier, vol. 238(1).
    218. Joachim Freyberger & Joel L. Horowitz, 2012. "Identification and shape restrictions in nonparametric instrumental variables estimation," CeMMAP working papers 15/12, Institute for Fiscal Studies.
    219. Sungwon Lee, 2024. "Partial identification and inference for conditional distributions of treatment effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 107-127, January.
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    1. Becker, Sascha O. & Hvide, Hans K., 2013. "Do entrepreneurs matter?," CEPR Discussion Papers 9295, C.E.P.R. Discussion Papers.
    2. Lee, Ji Hyung, 2016. "Predictive quantile regression with persistent covariates: IVX-QR approach," Journal of Econometrics, Elsevier, vol. 192(1), pages 105-118.
    3. Habert white & Tae-Hwan Kim & Simone Manganelli, 2012. "VAR for VaR: Measuring Tail Dependence Using Multivariate Regression Quantiles," Working papers 2012rwp-45, Yonsei University, Yonsei Economics Research Institute.
    4. Yuya Sasaki & Yulong Wang, 2024. "Extreme Quantile Treatment Effects under Endogeneity: Evaluating Policy Effects for the Most Vulnerable Individuals," Papers 2409.03979, arXiv.org.
    5. Calluzzo, Paul & Dong, Gang Nathan, 2015. "Has the financial system become safer after the crisis? The changing nature of financial institution risk," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 233-248.
    6. Hou, Yanxi & Leng, Xuan & Peng, Liang & Zhou, Yinggang, 2024. "Panel quantile regression for extreme risk," Journal of Econometrics, Elsevier, vol. 240(1).
    7. Florens, Jean-Pierre & Simar, Léopold & Van Keilegom, Ingrid, 2014. "Frontier estimation in nonparametric location-scale models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 456-470.
    8. Martina Pons, 2022. "The impact of air pollution on birthweight: evidence from grouped quantile regression," Empirical Economics, Springer, vol. 62(1), pages 279-296, January.
    9. Daouia, Abdelaati & Stupfler, Gilles & Usseglio-Carleve, Antoine, 2022. "Inference for extremal regression with dependent heavy-tailed data," TSE Working Papers 22-1324, Toulouse School of Economics (TSE), revised 29 Aug 2023.
    10. D’Haultfœuille, Xavier & Maurel, Arnaud & Zhang, Yichong, 2018. "Extremal quantile regressions for selection models and the black–white wage gap," Journal of Econometrics, Elsevier, vol. 203(1), pages 129-142.
    11. Wang, Yulong & Xiao, Zhijie, 2022. "Estimation and inference about tail features with tail censored data," Journal of Econometrics, Elsevier, vol. 230(2), pages 363-387.
    12. Martínez-Iriarte, Julián & Montes-Rojas, Gabriel & Sun, Yixiao, 2024. "Unconditional effects of general policy interventions," Journal of Econometrics, Elsevier, vol. 238(2).
    13. Valentina Corradi & Daniel Gutknecht, 2019. "Testing for Quantile Sample Selection," Papers 1907.07412, arXiv.org, revised Jan 2021.
    14. Yuya Sasaki & Yulong Wang, 2022. "Fixed-k Inference for Conditional Extremal Quantiles," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 829-837, April.
    15. Grigory Franguridi & Bulat Gafarov & Kaspar Wüthrich, 2021. "Conditional Quantile Estimators: A Small Sample Theory," CESifo Working Paper Series 9046, CESifo.
    16. Xavier D’Haultfoeuille & Arnaud Maurel & Xiaoyun Qiu & Yichong Zhang, 2020. "Estimating selection models without an instrument with Stata," Stata Journal, StataCorp LP, vol. 20(2), pages 297-308, June.
    17. David M. Kaplan, 2013. "Improved Quantile Inference Via Fixed-Smoothing Asymptotics And Edgeworth Expansion," Working Papers 1313, Department of Economics, University of Missouri.
    18. Denis Chetverikov & Bradley Larsen & Christopher Palmer, 2016. "IV Quantile Regression for Group‐Level Treatments, With an Application to the Distributional Effects of Trade," Econometrica, Econometric Society, vol. 84, pages 809-833, March.
    19. Daisuke Kurisu & Taisuke Otsu, 2021. "Nonparametric inference for extremal conditional quantiles," STICERD - Econometrics Paper Series 616, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    20. Vladislav Morozov, 2022. "Inference on Extreme Quantiles of Unobserved Individual Heterogeneity," Papers 2210.08524, arXiv.org, revised Jun 2023.
    21. Marilena Furno & Francesco Caracciolo, 2020. "Multi-valued Double Robust quantile treatment effect," Empirical Economics, Springer, vol. 58(5), pages 2545-2571, May.
    22. Michal Franta & Jan Vlcek, 2024. "Wage-Price Spirals: A Risk-Based Approach," Working Papers 2024/1, Czech National Bank.
    23. Kurisu, Daisuke & Otsu, Taisuke, 2023. "Subsampling inference for nonparametric extremal conditional quantiles," LSE Research Online Documents on Economics 120365, London School of Economics and Political Science, LSE Library.
    24. Victor Chernozhukov & Iv'an Fern'andez-Val & Blaise Melly, 2019. "Fast Algorithms for the Quantile Regression Process," Papers 1909.05782, arXiv.org, revised Apr 2020.
    25. Difang Huang & Jiti Gao & Tatsushi Oka, 2022. "Semiparametric Single-Index Estimation for Average Treatment Effects," Monash Econometrics and Business Statistics Working Papers 10/22, Monash University, Department of Econometrics and Business Statistics.
    26. Laura Garcia-Jorcano & Lidia Sanchis-Marco, 2023. "Measuring Systemic Risk Using Multivariate Quantile-Located ES Models," Journal of Financial Econometrics, Oxford University Press, vol. 21(1), pages 1-72.
    27. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    28. Yuya Sasaki & Yulong Wang, 2020. "Testing Finite Moment Conditions for the Consistency and the Root-N Asymptotic Normality of the GMM and M Estimators," Papers 2006.02541, arXiv.org, revised Sep 2020.
    29. Firpo, Sergio & Galvao, Antonio F. & Pinto, Cristine & Poirier, Alexandre & Sanroman, Graciela, 2022. "GMM quantile regression," Journal of Econometrics, Elsevier, vol. 230(2), pages 432-452.
    30. Zhengyu Zhang & Zequn Jin & Lihua Lin, 2024. "Identification and inference of outcome conditioned partial effects of general interventions," Papers 2407.16950, arXiv.org.
    31. Wei‐han Liu, 2020. "Are Gold and Government Bond Safe‐Haven Assets? An Extremal Quantile Regression Analysis," International Review of Finance, International Review of Finance Ltd., vol. 20(2), pages 451-483, June.
    32. Andres Sagner, 2020. "High Dimensional Quantile Factor Analysis," Working Papers Central Bank of Chile 886, Central Bank of Chile.
    33. Grimsby, Gjermund, 2018. "Partly risky, partly solid – Performance study of public innovation loans," Research Policy, Elsevier, vol. 47(7), pages 1344-1365.
    34. Denis Chetverikov & Yukun Liu & Aleh Tsyvinski, 2022. "Weighted-average quantile regression," Papers 2203.03032, arXiv.org.
    35. Oliver Himmler, 2009. "The Effects of School Competition on Academic Achievement and Grading Standards," CESifo Working Paper Series 2676, CESifo.
    36. David M. Kaplan, 2014. "Nonparametric Inference on Quantile Marginal Effects," Working Papers 1413, Department of Economics, University of Missouri.
    37. Chavleishvili, Sulkhan & Manganelli, Simone, 2019. "Forecasting and stress testing with quantile vector autoregression," Working Paper Series 2330, European Central Bank.
    38. Victor Chernozhukov & Iv'an Fern'andez-Val & Jonas Meier & Aico van Vuuren & Francis Vella, 2024. "Conditional Rank-Rank Regression," Papers 2407.06387, arXiv.org.
    39. Xiong, Qizhou, 2015. "Censored Fractional Response Model: Estimating Heterogeneous Relative Risk Aversion of European Households," IWH Discussion Papers 11/2015, Halle Institute for Economic Research (IWH).
    40. Aida Caldera Sánchez & Oliver Röhn, 2016. "How do policies influence GDP tail risks?," OECD Economics Department Working Papers 1339, OECD Publishing.
    41. Takuma Yoshida, 2021. "Additive models for extremal quantile regression with Pareto-type distributions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(1), pages 103-134, March.
    42. He, Fengyang & Wang, Huixia Judy & Zhou, Yuejin, 2022. "Extremal quantile autoregression for heavy-tailed time series," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).

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    Cited by:

    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    2. Giovanni Bonaccolto & Massimiliano Caporin & Sandra Paterlini, 2015. "Asset Allocation Strategies Based On Penalized Quantile Regression," "Marco Fanno" Working Papers 0199, Dipartimento di Scienze Economiche "Marco Fanno".
    3. Fan, Zengyan & Lian, Heng, 2018. "Quantile regression for additive coefficient models in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 164(C), pages 54-64.
    4. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    5. Jelena Bradic & Stefan Wager & Yinchu Zhu, 2019. "Sparsity Double Robust Inference of Average Treatment Effects," Papers 1905.00744, arXiv.org.
    6. Kenechukwu E. Anadu & James Bohn & Lina Lu & Matthew Pritsker & Andrei Zlate, 2019. "Reach for Yield by U.S. Public Pension Funds," Finance and Economics Discussion Series 2019-048, Board of Governors of the Federal Reserve System (U.S.).
    7. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    8. Sokbae Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2018. "Oracle Estimation of a Change Point in High-Dimensional Quantile Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1184-1194, July.
    9. Fang, Libing & Sun, Boyang & Li, Huijing & Yu, Honghai, 2018. "Systemic risk network of Chinese financial institutions," Emerging Markets Review, Elsevier, vol. 35(C), pages 190-206.
    10. Weihua Zhao & Jianbo Li & Heng Lian, 2018. "Adaptive varying-coefficient linear quantile model: a profiled estimating equations approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(3), pages 553-582, June.
    11. Sven Klaassen & Jannis Kueck & Martin Spindler, 2017. "Transformation Models in High-Dimensions," Papers 1712.07364, arXiv.org.
    12. Härdle, Wolfgang Karl & Ritov, Ya'acov & Song, Song, 2010. "Partial linear quantile regression and bootstrap confidence bands," SFB 649 Discussion Papers 2010-002, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    13. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Ivan Fernandez-Val, 2016. "Conditional quantile processes based on series or many regressors," CeMMAP working papers 46/16, Institute for Fiscal Studies.
    14. Pierre Alquier & Vincent Cottet & Guillaume Lecué, 2017. "Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions," Working Papers 2017-30, Center for Research in Economics and Statistics.
    15. Liqun Yu & Nan Lin, 2017. "ADMM for Penalized Quantile Regression in Big Data," International Statistical Review, International Statistical Institute, vol. 85(3), pages 494-518, December.
    16. Max H. Farrell, 2013. "Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations," Papers 1309.4686, arXiv.org, revised Feb 2018.
    17. Giovanni Bonaccolto, 2019. "Critical Decisions for Asset Allocation via Penalized Quantile Regression," Papers 1908.04697, arXiv.org.
    18. Zbonakova, L. & Härdle, W.K. & Wang, W., 2016. "Time Varying Quantile Lasso," Working Papers 16/07, Department of Economics, City University London.
    19. John W. Galbraith & Victoria Zinde-Walsh, 2011. "Partially Dimension-Reduced Regressions with Potentially Infinite-Dimensional Processes," CIRANO Working Papers 2011s-57, CIRANO.
    20. Wolfgang Karl Härdle & David Kuo Chuen Lee & Sergey Nasekin & Alla Petukhina, 2018. "Tail Event Driven ASset allocation: evidence from equity and mutual funds’ markets," Journal of Asset Management, Palgrave Macmillan, vol. 19(1), pages 49-63, January.
    21. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    22. Demian Pouzo, 2015. "On the Non-Asymptotic Properties of Regularized M-estimators," Papers 1512.06290, arXiv.org, revised Oct 2016.
    23. Jianqing Fan & Quefeng Li & Yuyan Wang, 2017. "Estimation of high dimensional mean regression in the absence of symmetry and light tail assumptions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 247-265, January.
    24. Florens, Jean-Pierre & Van Bellegem, Sébastien, 2015. "Instrumental variable estimation in functional linear models," Journal of Econometrics, Elsevier, vol. 186(2), pages 465-476.
    25. Ali Aghamohammadi, 2018. "Bayesian analysis of dynamic panel data by penalized quantile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(1), pages 91-108, March.

  132. Victor Chernozhukov & Ivan Fernandez-Val & Jinyong Hahn & Whitney Newey, 2009. "Average and Quantile Effects in Nonseparable Panel Models," Papers 0904.1990, arXiv.org, revised Mar 2013.

    Cited by:

    1. Ariel Pakes & Jack Porter, 2024. "Moment inequalities for multinomial choice with fixed effects," Quantitative Economics, Econometric Society, vol. 15(1), pages 1-25, January.
    2. Ivan Fernandez-Val & Martin Weidner, 2013. "Individual and Time Effects in Nonlinear Panel Models with Large N, T," Papers 1311.7065, arXiv.org, revised Dec 2018.
    3. Demetrescu, Matei & Hosseinkouchack, Mehdi & Rodrigues, Paulo M. M., 2023. "Tests of no cross-sectional error dependence in panel quantile regressions," Ruhr Economic Papers 1041, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    4. Ivan Fernandez-Val & Hugo Freeman & Martin Weidner, 2020. "Low-rank approximations of nonseparable panel models," CeMMAP working papers CWP52/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Kate Ho & Adam M. Rosen, 2015. "Partial Identification in Applied Research: Benefits and Challenges," NBER Working Papers 21641, National Bureau of Economic Research, Inc.
    6. Christopher Dobronyi & Jiaying Gu & Kyoo il Kim, 2021. "Identification of Dynamic Panel Logit Models with Fixed Effects," Papers 2104.04590, arXiv.org, revised Apr 2021.
    7. Brantly Callaway & Tong Li, 2019. "Quantile treatment effects in difference in differences models with panel data," Quantitative Economics, Econometric Society, vol. 10(4), pages 1579-1618, November.
    8. Kevin Dano, 2023. "Transition Probabilities and Moment Restrictions in Dynamic Fixed Effects Logit Models," Papers 2303.00083, arXiv.org, revised Dec 2023.
    9. Pedro H. C. Sant'Anna & Jun B. Zhao, 2018. "Doubly Robust Difference-in-Differences Estimators," Papers 1812.01723, arXiv.org, revised May 2020.
    10. Desire Kedagni & Kala Krishna & Rigissa Megalokonomou & Yingyan Zhao, 2019. "Does Class Size Matter? How, and at What Cost?," NBER Working Papers 25736, National Bureau of Economic Research, Inc.
    11. Shu Yang & Yunshu Zhang, 2023. "Multiply robust matching estimators of average and quantile treatment effects," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(1), pages 235-265, March.
    12. Victor Chernozhukov & Ivan Fernandez-Val & Stefan Hoderlein & Hajo Holzmann & Whitney K. Newey, 2013. "Nonparametric identification in panels using quantiles," CeMMAP working papers 66/13, Institute for Fiscal Studies.
    13. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment," DETU Working Papers 1804, Department of Economics, Temple University.
    14. Botosaru, Irene & Muris, Chris & Pendakur, Krishna, 2023. "Identification of time-varying transformation models with fixed effects, with an application to unobserved heterogeneity in resource shares," Journal of Econometrics, Elsevier, vol. 232(2), pages 576-597.
    15. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods," Papers 1803.09015, arXiv.org, revised Dec 2020.
    16. Clément de Chaisemartin & Xavier D’haultfœuille, 2022. "Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey," Post-Print hal-03873885, HAL.
    17. Ghanem, Dalia, 2017. "Testing identifying assumptions in nonseparable panel data models," Journal of Econometrics, Elsevier, vol. 197(2), pages 202-217.
    18. Pauline Givord & Lionel Wilner, 2015. "When Does the Stepping‐Stone Work? Fixed‐Term Contracts Versus Temporary Agency Work in Changing Economic Conditions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(5), pages 787-805, August.
    19. Desbordes, Rodolphe & Eberhardt, Markus, 2024. "Climate change and economic prosperity: Evidence from a flexible damage function," Journal of Environmental Economics and Management, Elsevier, vol. 125(C).
    20. Michelle Marcus & Pedro H. C. Sant’Anna, 2021. "The Role of Parallel Trends in Event Study Settings: An Application to Environmental Economics," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 8(2), pages 235-275.
    21. Xavier D'Haultfoeuille & Stefan Hoderlein & Yuya Sasaki, 2013. "Nonlinear Difference-in-Differences in Repeated Cross Sections with Continuous Treatments," Boston College Working Papers in Economics 839, Boston College Department of Economics.
    22. Xin Liu, 2024. "A quantile-based nonadditive fixed effects model," Papers 2405.03826, arXiv.org.
    23. Bryan S. Graham & Jinyong Hahn & Alexandre Poirier & James L. Powell, 2016. "A quantile correlated random coefficients panel data model," CeMMAP working papers 34/16, Institute for Fiscal Studies.
    24. Traferri, Alejandra, 2011. "State dependence and heterogeneity in health using a bias corrected fixed effects estimator," UC3M Working papers. Economics we1118, Universidad Carlos III de Madrid. Departamento de Economía.
    25. Khan, Shakeeb & Ponomareva, Maria & Tamer, Elie, 2011. "Identification of Panel Data Models with Endogenous Censoring," MPRA Paper 30373, University Library of Munich, Germany.
    26. Jackson Bunting, 2022. "Continuous permanent unobserved heterogeneity in dynamic discrete choice models," Papers 2202.03960, arXiv.org, revised Feb 2024.
    27. Shakeeb Khan & Fu Ouyang & Elie Tamer, 2020. "Inference on Semiparametric Multinomial Response Models," Discussion Papers Series 627, School of Economics, University of Queensland, Australia.
    28. Victor Aguirregabiria, 2023. "Dynamic demand for differentiated products with fixed-effects unobserved heterogeneity," The Econometrics Journal, Royal Economic Society, vol. 26(1), pages 1-25.
    29. Andrew Chesher & Adam Rosen & Yuanqi Zhang, 2023. "Identification analysis in models with unrestricted latent variables: Fixed effects and initial conditions," CeMMAP working papers 20/23, Institute for Fiscal Studies.
    30. Williams, Benjamin, 2020. "Nonparametric identification of discrete choice models with lagged dependent variables," Journal of Econometrics, Elsevier, vol. 215(1), pages 286-304.
    31. Raf Van Gestel & Tobias Müller & Johan Bosmans, 2018. "Learning from failure in healthcare: Dynamic panel evidence of a physician shock effect," Health Economics, John Wiley & Sons, Ltd., vol. 27(9), pages 1340-1353, September.
    32. Cl'ement de Chaisemartin & Diego Ciccia Xavier D'Haultf{oe}uille & Felix Knau, 2024. "Two-way Fixed Effects and Differences-in-Differences Estimators in Heterogeneous Adoption Designs," Papers 2405.04465, arXiv.org, revised Nov 2024.
    33. Cheng Hsiao & Qi Li & Zhongwen Liang & Wei Xie, 2019. "Panel Data Estimation for Correlated Random Coefficients Models," Econometrics, MDPI, vol. 7(1), pages 1-18, February.
    34. Benjamin Williams, 2018. "Identification of a Nonseparable Model under Endogeneity using Binary Proxies for Unobserved Heterogeneity," Working Papers 2018-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    35. Yingyao Hu & Ji‐Liang Shiu, 2018. "Identification and estimation of semi‐parametric censored dynamic panel data models of short time periods," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 55-85, February.
    36. Tymon S{l}oczy'nski, 2018. "Interpreting OLS Estimands When Treatment Effects Are Heterogeneous: Smaller Groups Get Larger Weights," Papers 1810.01576, arXiv.org, revised May 2020.
    37. Dalia Ghanem & Pedro H. C. Sant'Anna & Kaspar Wuthrich, 2022. "Selection and parallel trends," Papers 2203.09001, arXiv.org, revised Mar 2024.
    38. Sloczynski, Tymon, 2018. "A General Weighted Average Representation of the Ordinary and Two-Stage Least Squares Estimands," IZA Discussion Papers 11866, Institute of Labor Economics (IZA).
    39. Callaway, Brantly & Li, Tong & Oka, Tatsushi, 2018. "Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods," Journal of Econometrics, Elsevier, vol. 206(2), pages 395-413.
    40. Manuel Arellano & Stéphane Bonhomme, 2009. "Identifying distributional characteristics in random coefficients panel data models," CeMMAP working papers CWP22/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    41. Cassidy, Rachel & Groot Bruinderink, Marije & Janssens, Wendy & Morsink, Karlijn, 2021. "The power to protect: Household bargaining and female condom use," Journal of Development Economics, Elsevier, vol. 153(C).
    42. Matthew Harding & Carlos Lamarche, 2018. "A Panel Quantile Approach to Attrition Bias in Big Data: Evidence from a Randomized Experiment," Papers 1808.03364, arXiv.org.
    43. Dmitry Arkhangelsky & Guido W. Imbens, 2021. "Double-Robust Identification for Causal Panel Data Models," NBER Working Papers 28364, National Bureau of Economic Research, Inc.
    44. Irene Botosaru & Raffaella Giacomini & Martin Weidner, 2023. "Forecasted Treatment Effects," Papers 2309.05639, arXiv.org, revised Jan 2024.
    45. Ghanem, Dalia & Zhang, Junjie, 2014. "‘Effortless Perfection:’ Do Chinese cities manipulate air pollution data?," Journal of Environmental Economics and Management, Elsevier, vol. 68(2), pages 203-225.
    46. Xiao, Zhijie & Xu, Lan, 2019. "What do mean impacts miss? Distributional effects of corporate diversification," Journal of Econometrics, Elsevier, vol. 213(1), pages 92-120.
    47. Aguirregabiria, Victor & Gu, Jiaying & Luo, Yao, 2018. "Sufficient Statistics for Unobserved Heterogeneity in Structural Dynamic Logit Models," CEPR Discussion Papers 12930, C.E.P.R. Discussion Papers.
    48. Louise Laage, 2020. "A Correlated Random Coefficient Panel Model with Time-Varying Endogeneity," Papers 2003.09367, arXiv.org, revised Nov 2022.
    49. Mikkel Plagborg-Moller & Gita Gopinath & Emine Boz, 2017. "Global Trade and the Dollar," 2017 Meeting Papers 1041, Society for Economic Dynamics.
    50. Antonio F. Galvao & Thomas Parker & Zhijie Xiao, 2021. "Bootstrap inference for panel data quantile regression," Papers 2111.03626, arXiv.org.
    51. Bo E. Honoré & Áureo de Paula, 2021. "Identification in simple binary outcome panel data models," CeMMAP working papers CWP14/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    52. Victor Chernozhukov & Iv'an Fern'andez-Val & Martin Weidner, 2018. "Network and Panel Quantile Effects Via Distribution Regression," Papers 1803.08154, arXiv.org, revised Jun 2020.
    53. Iv'an Fern'andez-Val & Hugo Freeman & Martin Weidner, 2020. "Low-Rank Approximations of Nonseparable Panel Models," Papers 2010.12439, arXiv.org, revised Mar 2021.
    54. Ivan Fernandez-Val & Martin Weidner, 2015. "Individual and time effects in nonlinear panel models with large N , T," CeMMAP working papers 17/15, Institute for Fiscal Studies.
    55. Antonio F. Galvao & Jiaying Gu & Stanislav Volgushev, 2018. "On the Unbiased Asymptotic Normality of Quantile Regression with Fixed Effects," Papers 1807.11863, arXiv.org, revised Feb 2020.
    56. Manuel Arellano & Stéphane Bonhomme, 2015. "Nonlinear panel data estimation via quantile regressions," CeMMAP working papers 40/15, Institute for Fiscal Studies.
    57. Timothy Christensen & Hyungsik Roger Moon & Frank Schorfheide, 2022. "Optimal Decision Rules when Payoffs are Partially Identified," Papers 2204.11748, arXiv.org, revised May 2023.
    58. St'ephane Bonhomme & Martin Weidner, 2018. "Minimizing Sensitivity to Model Misspecification," Papers 1807.02161, arXiv.org, revised Oct 2021.
    59. Khan, S. & Ponomareva, M. & Tamer, E., 2023. "Identification of dynamic binary response models," Journal of Econometrics, Elsevier, vol. 237(1).
    60. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey, 2017. "Nonseparable multinomial choice models in cross-section and panel data," CeMMAP working papers 33/17, Institute for Fiscal Studies.
    61. Wooldridge, Jeffrey M., 2019. "Correlated random effects models with unbalanced panels," Journal of Econometrics, Elsevier, vol. 211(1), pages 137-150.
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    63. Stéphane Bonhomme & Martin Weidner, 2020. "Minimizing Sensitivity to Model Misspecification," CeMMAP working papers CWP37/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    64. Andrew Chesher & Adam Rosen & Yuanqi Zhang, 2024. "Robust analysis of short panels," IFS Working Papers WCWP01/24, Institute for Fiscal Studies.
    65. Johannes S. Kunz & Kevin E. Staub & Rainer Winkelmann, 2021. "Predicting individual effects in fixed effects panel probit models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 1109-1145, July.
    66. Sadikoglu, Serhan, 2019. "Essays in econometric theory," Other publications TiSEM 99d83644-f9dc-49e3-a4e1-5, Tilburg University, School of Economics and Management.
    67. Bartolucci, Francesco & Pigini, Claudia & Valentini, Francesco, 2021. "Conditional inference and bias reduction for partial effects estimation of fixed-effects logit models," MPRA Paper 110031, University Library of Munich, Germany.
    68. Laurent Davezies & Xavier D'Haultfoeuille & Martin Mugnier, 2020. "Fixed Effects Binary Choice Models with Three or More Periods," Papers 2009.08108, arXiv.org, revised Sep 2022.
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    118. Timothy Christensen & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Robust Forecasting," PIER Working Paper Archive 20-038, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
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    122. Douglas L. Miller & Na’ama Shenhav & Michel Grosz, 2023. "Selection into Identification in Fixed Effects Models, with Application to Head Start," Journal of Human Resources, University of Wisconsin Press, vol. 58(5), pages 1523-1566.
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    124. Kosuke Imai & In Song Kim, 2019. "When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data?," American Journal of Political Science, John Wiley & Sons, vol. 63(2), pages 467-490, April.
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    127. St'ephane Bonhomme & Kevin Dano, 2023. "Functional Differencing in Networks," Papers 2307.11484, arXiv.org.
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    130. Yuya Sasaki & Takuya Ura, 2021. "Slow Movers in Panel Data," Papers 2110.12041, arXiv.org.
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    144. Michael P. Leung & Hyungsik Roger Moon, 2019. "Normal Approximation in Large Network Models," Papers 1904.11060, arXiv.org, revised Oct 2024.
    145. Geert Dhaene & Martin Weidner, 2023. "Approximate Functional Differencing," Papers 2301.13736, arXiv.org, revised May 2023.
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    Cited by:

    1. Gallant, A. Ronald & Hong, Han & Leung, Michael P. & Li, Jessie, 2022. "Constrained estimation using penalization and MCMC," Journal of Econometrics, Elsevier, vol. 228(1), pages 85-106.

  134. Daron Acemoglu & Victor Chernozhukov & Muhamet Yildiz, 2009. "Fragility of Asymptotic Agreement under Bayesian Learning," Levine's Working Paper Archive 814577000000000139, David K. Levine.

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    1. Antonio Jiménez-Martínez, 2014. "A model of belief influence in large social networks," Working Papers DTE 572, CIDE, División de Economía.
    2. Jean-Pierre Benoît & Juan Dubra, 2018. "When do populations polarize? An explanation," Documentos de Trabajo/Working Papers 1801, Facultad de Ciencias Empresariales y Economia. Universidad de Montevideo..
    3. Piotr Evdokimov & Umberto Garfagnini, 2022. "Higher-order learning," Experimental Economics, Springer;Economic Science Association, vol. 25(4), pages 1234-1266, September.
    4. Kenneth J. Singleton, 2021. "Presidential Address: How Much “Rationality” Is There in Bond‐Market Risk Premiums?," Journal of Finance, American Finance Association, vol. 76(4), pages 1611-1654, August.
    5. Levy, Gilat & Razin, Ronny, 2021. "A maximum likelihood approach to combining forecasts," LSE Research Online Documents on Economics 104116, London School of Economics and Political Science, LSE Library.
    6. George J. Mailath & Larry Samuelson, 2019. "Learning under Diverse World Views: Model-Based Inference," Cowles Foundation Discussion Papers 2161R, Cowles Foundation for Research in Economics, Yale University, revised Sep 2019.
    7. Wei Xiong, 2013. "Bubbles, Crises, and Heterogeneous Beliefs," NBER Working Papers 18905, National Bureau of Economic Research, Inc.
    8. Mira Frick & Ryota Iijima & Yuhta Ishii, 2021. "Learning Efficiency of Multi-Agent Information Structures," Cowles Foundation Discussion Papers 2299R, Cowles Foundation for Research in Economics, Yale University, revised Dec 2021.
    9. Alice Hsiaw & Ing-Haw Cheng, 2016. "Distrust in Experts and the Origins of Disagreement," Working Papers 110R3, Brandeis University, Department of Economics and International Business School, revised Mar 2018.
    10. Rajiv Sethi & Muhamet Yildiz, 2009. "Public Disagreement," Economics Working Papers 0089, Institute for Advanced Study, School of Social Science.
    11. Gabriel Martinez & Nicholas H. Tenev, 2020. "Optimal Echo Chambers," Papers 2010.01249, arXiv.org, revised Feb 2024.
    12. Hestermann, Nina & Le Yaouanq, Yves, 2018. "It\'s not my Fault! Self-Confidence and Experimentation," Rationality and Competition Discussion Paper Series 124, CRC TRR 190 Rationality and Competition.
    13. Mandler, Michael, 2012. "The fragility of information aggregation in large elections," Games and Economic Behavior, Elsevier, vol. 74(1), pages 257-268.
    14. José Luis Montiel Olea & Pietro Ortoleva & Mallesh Pai & Andrea Prat, 2021. "Competing Models," Working Papers 2021-89, Princeton University. Economics Department..
    15. Prat, Andrea & Montiel Olea , José Luis & Ortoleva, Pietro & Pai, Mallesh, 2019. "Competing Models," CEPR Discussion Papers 14066, C.E.P.R. Discussion Papers.
      • Jose Luis Montiel Olea & Pietro Ortoleva & Mallesh M Pai & Andrea Prat, 2019. "Competing Models," Papers 1907.03809, arXiv.org, revised Nov 2021.
    16. J. Aislinn Bohren, 2013. "Informational Herding with Model Misspecification," PIER Working Paper Archive 14-007, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    17. Leung, B. T. K., 2020. "Learning in a Small/Big World," Cambridge Working Papers in Economics 2085, Faculty of Economics, University of Cambridge.
    18. Ing-Haw Cheng & Alice Hsiaw, 2023. "Bayesian Doublespeak," Working Papers 135, Brandeis University, Department of Economics and International Business School.
    19. S. Nageeb Ali, 2009. "Learning Self-Control," Levine's Working Paper Archive 814577000000000384, David K. Levine.
    20. de la Torre, Augusto & Ize, Alain, 2013. "The foundations of macroprudential regulation : a conceptual roadmap," Policy Research Working Paper Series 6575, The World Bank.
    21. Isaac Loh & Gregory Phelan, 2019. "Dimensionality And Disagreement: Asymptotic Belief Divergence In Response To Common Information," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 60(4), pages 1861-1876, November.
    22. Le Yaouanq, Yves, 2018. "A Model of Ideological Thinking," Rationality and Competition Discussion Paper Series 85, CRC TRR 190 Rationality and Competition.
    23. Stephen Morris & Muhamet Yildiz, 2019. "Crises: Equilibrium Shifts and Large Shocks," American Economic Review, American Economic Association, vol. 109(8), pages 2823-2854, August.
    24. Kondor, Péter, 2011. "The more we know on the fundamental, the less we agree on the price," CEPR Discussion Papers 8455, C.E.P.R. Discussion Papers.
    25. Carsten Hefeker & Michael Neugart, 2019. "Policy Coordination under Model Disagreement and Uncertainty," CESifo Working Paper Series 7477, CESifo.
    26. Mira Frick & Ryota Iijima & Yuhta Ishii, 2019. "Misinterpreting Others and the Fragility of Social Learning," Cowles Foundation Discussion Papers 2160R, Cowles Foundation for Research in Economics, Yale University, revised Mar 2020.
    27. Larry G Epstein & Yoram Halevy, 2024. "Hard-to-Interpret Signals," Journal of the European Economic Association, European Economic Association, vol. 22(1), pages 393-427.
    28. Hefeker, Carsten, 2022. "Policy coordination under model disagreement and asymmetric shocks," Economic Modelling, Elsevier, vol. 114(C).
    29. Benson Tsz Kin Leung, 2020. "Learning in a Small/Big World," Papers 2009.11917, arXiv.org, revised Mar 2023.
    30. Emilien Macault, 2022. "Stochastic Consensus and the Shadow of Doubt," Papers 2201.12100, arXiv.org.
    31. Adriani, Fabrizio & Sonderegger, Silvia, 2009. "Trust, Introspection, and Market Participation: an Evolutionary Approach," MPRA Paper 16110, University Library of Munich, Germany.
    32. Stone, Daniel, 2018. "Just a big misunderstanding? Bias and Bayesian affective polarization," SocArXiv 58sru, Center for Open Science.
    33. Gieczewski, Germán, 2022. "Verifiable communication on networks," Journal of Economic Theory, Elsevier, vol. 204(C).
    34. Matthew Gentzkow & Jesse M. Shapiro, 2011. "Ideological Segregation Online and Offline," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 126(4), pages 1799-1839.
    35. Larry G. Epstein & Kyoungwon Seo, 2013. "De Finetti Meets Ellsberg," CIRANO Working Papers 2013s-35, CIRANO.
    36. Sandeep Baliga & Eran Hanany & Peter Klibanoff, 2013. "Polarization and Ambiguity," American Economic Review, American Economic Association, vol. 103(7), pages 3071-3083, December.
    37. Floyd Jiuyun Zhang, 2023. "Political endorsement by Nature and trust in scientific expertise during COVID-19," Nature Human Behaviour, Nature, vol. 7(5), pages 696-706, May.
    38. Meng, Delong & Wang, Siyu, 2024. "Impact of open-mindedness on information avoidance: Tailored vs. generic communication," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 108(C).
    39. Fulghieri, Paolo & Dicks, David, 2015. "Ambiguity, Disagreement, and Allocation of Control in Firms," CEPR Discussion Papers 10400, C.E.P.R. Discussion Papers.
    40. Antonio Jiménez-Martínez, 2012. "Consensus in Communication Networks under Bayesian Updating," Working Papers DTE 529, CIDE, División de Economía.
    41. Dani Rodrik, 2013. "When Ideas Trump Interests : Preferences, World Views, and Policy  Innovations," Working Papers id:5558, eSocialSciences.
    42. Ing-Haw Cheng & Alice Hsiaw, 2016. "Trust in Signals and the Origins of Disagreement," Working Papers 110R4, Brandeis University, Department of Economics and International Business School, revised Dec 2018.
    43. Azomahou, T. & Opolot, D., 2014. "Beliefs dynamics in communication networks," MERIT Working Papers 2014-034, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    44. Benoît, Jean-Pierre & Dubra, Juan, 2014. "A Theory of Rational Attitude Polarization," MPRA Paper 60129, University Library of Munich, Germany.
    45. Ceren Baysan, 2017. "Can More Information Lead to More Voter Polarization? Experimental Evidence from Turkey," 2017 Papers pba1551, Job Market Papers.
    46. Jeong, Daeyoung, 2019. "Using cheap talk to polarize or unify a group of decision makers," Journal of Economic Theory, Elsevier, vol. 180(C), pages 50-80.
    47. Rajiv Sethi & Muhamet Yildiz, 2013. "Perspectives, Opinions, and Information Flows," Levine's Working Paper Archive 786969000000000934, David K. Levine.
    48. Gabaix, Xavier & Laibson, David & Li, Deyuan & Li, Hongyi & Resnick, Sidney & de Vries, Casper G., 2016. "The impact of competition on prices with numerous firms," Journal of Economic Theory, Elsevier, vol. 165(C), pages 1-24.
    49. Le Yaouanq, Yves, 2023. "A model of voting with motivated beliefs," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 394-408.
    50. Pai, Mallesh & Hansen, Karsten, 2020. "Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms," CEPR Discussion Papers 14372, C.E.P.R. Discussion Papers.
    51. Simsek, Alp, 2012. "Belief Disagreements and Collateral Constraints," Scholarly Articles 9561259, Harvard University Department of Economics.
    52. Mohamed Mostagir & James Siderius, 2022. "Learning in a Post-Truth World," Management Science, INFORMS, vol. 68(4), pages 2860-2868, April.
    53. In-Koo Cho & Kenneth Kasa, 2017. "Model Averaging and Persistent Disagreement," Review, Federal Reserve Bank of St. Louis, vol. 99(3), pages 279-294.
    54. Fudenberg, Drew & Lanzani, Giacomo & Strack, Philipp, 2023. "Pathwise concentration bounds for Bayesian beliefs," Theoretical Economics, Econometric Society, vol. 18(4), November.
    55. Liu, Qi & Sun, Bo, 2018. "Managerial manipulation, corporate governance, and limited market participation," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 98-117.

  135. Victor Chernozhukov & Patrick Gagliardini & Olivier Scaillet, 2008. "Nonparametric Instrumental Variable Estimators of Structural Quantile Effects," Swiss Finance Institute Research Paper Series 08-03, Swiss Finance Institute, revised Aug 2009.

    Cited by:

    1. Xiaohong Chen & Demian Pouzo, 2012. "Estimation of Nonparametric Conditional Moment Models With Possibly Nonsmooth Generalized Residuals," Econometrica, Econometric Society, vol. 80(1), pages 277-321, January.
    2. Florens, Jean-Pierre & Simon, Guillaume, 2010. "Endogeneity and Instrumental Variables in Dynamic Models," TSE Working Papers 10-178, Toulouse School of Economics (TSE).
    3. Senay Sokullu, 2012. "Nonparametric Analysis of Two-Sided Markets," Bristol Economics Discussion Papers 12/628, School of Economics, University of Bristol, UK.
    4. Carrasco, Marine & Florens, Jean-Pierre, 2002. "Spectral Method for Deconvolving a Density," IDEI Working Papers 138, Institut d'Économie Industrielle (IDEI), Toulouse, revised 2009.
    5. Xiaohong Chen & Demian Pouzo, 2008. "Estimation of nonparametric conditional moment models with possibly nonsmooth moments," CeMMAP working papers CWP12/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Juan Carlos Escanciano & Wei Li, 2013. "On the identification of structural linear functionals," CeMMAP working papers 48/13, Institute for Fiscal Studies.

  136. Victor Chernozhukov & Ivan Fernandez-Val & Alfred Galichon, 2008. "Improving Point and Interval Estimates of Monotone Functions by Rearrangement," Papers 0806.4730, arXiv.org, revised Nov 2008.

    Cited by:

    1. Wu, Ximing & Sickles, Robin, 2018. "Semiparametric estimation under shape constraints," Econometrics and Statistics, Elsevier, vol. 6(C), pages 74-89.
    2. Tae-Hwy Lee & Weiping Yang, 2014. "Granger-Causality in Quantiles between Financial Markets: Using Copula Approach," Working Papers 201406, University of California at Riverside, Department of Economics.
    3. Sokbae Lee & Ryo Okui & Yoon†Jae Whang, 2017. "Doubly robust uniform confidence band for the conditional average treatment effect function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1207-1225, November.
    4. Joel L. Horowitz & Sokbae (Simon) Lee, 2010. "Uniform confidence bands for functions estimated nonparametrically with instrumental variables," CeMMAP working papers CWP19/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Stéphane Bonhomme & Koen Jochmans & Jean-Marc Robin, 2017. "Nonparametric estimation of non-exchangeable latent-variable models," SciencePo Working papers Main hal-03264006, HAL.
    6. Amy Finkelstein & Erzo F.P. Luttmer & Matthew J. Notowidigdo, 2008. "What Good Is Wealth Without Health? The Effect of Health on the Marginal Utility of Consumption," NBER Working Papers 14089, National Bureau of Economic Research, Inc.
    7. José F. Tudón M., 2019. "Perception, utility, and evolution," Economic Theory Bulletin, Springer;Society for the Advancement of Economic Theory (SAET), vol. 7(2), pages 191-208, December.
    8. Victor Chernozhukov & Ivan Fernandez-Val & Alfred Galichon, 2007. "Quantile and probability curves without crossing," CeMMAP working papers CWP10/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Catania, Leopoldo & Luati, Alessandra, 2023. "Semiparametric modeling of multiple quantiles," Journal of Econometrics, Elsevier, vol. 237(2).
    10. Fan, Zengyan & Lian, Heng, 2018. "Quantile regression for additive coefficient models in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 164(C), pages 54-64.
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    13. Joel L. Horowitz & Sokbae (Simon) Lee, 2015. "Nonparametric estimation and inference under shape restrictions," CeMMAP working papers CWP67/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    16. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly, 2012. "Inference on counterfactual distributions," CeMMAP working papers 05/12, Institute for Fiscal Studies.
    17. Ryo Okui & Takahide Yanagi, 2018. "Kernel Estimation for Panel Data with Heterogeneous Dynamics," Papers 1802.08825, arXiv.org, revised May 2019.
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    20. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly & Kaspar Wüthrich, 2017. "Generic inference on quantile and quantile effect functions for discrete outcomes," CeMMAP working papers CWP23/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    21. Senay Sokullu, 2012. "Nonparametric Analysis of Two-Sided Markets," Bristol Economics Discussion Papers 12/628, School of Economics, University of Bristol, UK.
    22. Koen Jochmans, 2013. "Pairwise-comparison estimation with non-parametric controls," Post-Print hal-03399882, HAL.
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    24. Ilaria Lucrezia Amerise, 2013. "Weighted Non-Crossing Quantile Regressions," Working Papers 201308, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    25. Rodrigues, T. & Dortet-Bernadet, J.-L. & Fan, Y., 2019. "Simultaneous fitting of Bayesian penalised quantile splines," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 93-109.
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    28. Victor Chernozhukov & Whitney K. Newey & Andres Santos, 2015. "Constrained conditional moment restriction models," CeMMAP working papers 59/15, Institute for Fiscal Studies.
    29. Denis Chetverikov & Daniel Wilhelm, 2016. "Nonparametric instrumental variable estimation under monotonicity," CeMMAP working papers 48/16, Institute for Fiscal Studies.
    30. Florent Dubois, 2017. "The Sources of Segregation," AMSE Working Papers 1720, Aix-Marseille School of Economics, France.
    31. Gabriel Montes-Rojas & Lucas Siga & Ram Mainali, 2017. "Mean and quantile regression Oaxaca-Blinder decompositions with an application to caste discrimination," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 15(3), pages 245-255, September.
    32. Tatsushi Oka & Shota Yasui & Yuta Hayakawa & Undral Byambadalai, 2024. "Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trials," Papers 2407.14074, arXiv.org.
    33. Victor Chernozhukov & Iv'an Fern'andez-Val & Martin Weidner, 2018. "Network and Panel Quantile Effects Via Distribution Regression," Papers 1803.08154, arXiv.org, revised Jun 2020.
    34. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2024. "Inflation Target at Risk: A Time-varying Parameter Distributional Regression," Papers 2403.12456, arXiv.org.
    35. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Ivan Fernandez-Val, 2016. "Conditional quantile processes based on series or many regressors," CeMMAP working papers 46/16, Institute for Fiscal Studies.
    36. Thomas R. Covert & Richard L. Sweeney, 2023. "Relinquishing Riches: Auctions versus Informal Negotiations in Texas Oil and Gas Leasing," American Economic Review, American Economic Association, vol. 113(3), pages 628-663, March.
    37. Henderson, Daniel J. & List, John A. & Millimet, Daniel L. & Parmeter, Christopher F. & Price, Michael K., 2012. "Empirical implementation of nonparametric first-price auction models," Journal of Econometrics, Elsevier, vol. 168(1), pages 17-28.
    38. Marc Henry & Romuald Meango & Ismael Mourifie, 2020. "Role models and revealed gender-specific costs of STEM in an extended Roy model of major choice," Papers 2005.09095, arXiv.org, revised Aug 2023.
    39. Trabs, Mathias, 2011. "Calibration of self-decomposable Lévy models," SFB 649 Discussion Papers 2011-073, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    40. Tatiana Komarova & Javier Hidalgo, 2019. "Testing nonparametric shape restrictions," Papers 1909.01675, arXiv.org, revised Jun 2020.
    41. David H. Autor & Susan N. Houseman & Sari Pekkala Kerr, 2012. "The Effect of Work First Job Placements on the Distribution of Earnings: An Instrumental Variable Quantile Regression Approach," NBER Working Papers 17972, National Bureau of Economic Research, Inc.
    42. Ghossoub, Mario, 2011. "Monotone equimeasurable rearrangements with non-additive probabilities," MPRA Paper 37629, University Library of Munich, Germany, revised 23 Mar 2012.
    43. Stanislav Anatolyev & Jozef Barunik, 2017. "Forecasting dynamic return distributions based on ordered binary choice," Papers 1711.05681, arXiv.org, revised Jan 2019.
    44. Avdulaj Krenar & Barunik Jozef, 2017. "A semiparametric nonlinear quantile regression model for financial returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(1), pages 81-97, February.
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    47. Chernozhukov, Victor & Wüthrich, Kaspar & Zhu, Yinchu, 2021. "Distributional conformal prediction," University of California at San Diego, Economics Working Paper Series qt2zs6m5p5, Department of Economics, UC San Diego.
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    52. Victor Chernozhukov & Iván Fernández‐Val & Ye Luo, 2018. "The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages," Econometrica, Econometric Society, vol. 86(6), pages 1911-1938, November.
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    1. Romuald Meango & Esther Mirjam Girsberger, 2023. "Identification of Ex ante Returns Using Elicited Choice Probabilities: an Application to Preferences for Public-sector Jobs," Papers 2303.03009, arXiv.org, revised Jun 2024.
    2. Nicodemo, Catia & Raya, Josep M., 2012. "Change in the Distribution of House Prices across Spanish Cities," IZA Discussion Papers 6503, Institute of Labor Economics (IZA).
    3. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "On the asymptotic theory for least squares series: pointwise and uniform results," CeMMAP working papers 73/13, Institute for Fiscal Studies.
    4. Ivan Fernandez-Val & Hugo Freeman & Martin Weidner, 2020. "Low-rank approximations of nonseparable panel models," CeMMAP working papers CWP52/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Gaglianone, Wagner Piazza & Guillén, Osmani Teixeira de Carvalho & Figueiredo, Francisco Marcos Rodrigues, 2018. "Estimating inflation persistence by quantile autoregression with quantile-specific unit roots," Economic Modelling, Elsevier, vol. 73(C), pages 407-430.
    6. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey & Sami Stouli & Francis Vella, 2017. "Semiparametric estimation of structural functions in nonseparable triangular models," CeMMAP working papers CWP48/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    10. Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
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    33. Victor Chernozhukov & Ivan Fernandez-Val & Siyi Luo, 2018. "Distribution regression with sample selection, with an application to wage decompositions in the UK," CeMMAP working papers CWP68/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    34. Ruhose, Jens & Parey, Matthias & Waldinger, Fabian & Netz, Nicolai, 2015. "The Selection of High-Skilled Migrants," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113148, Verein für Socialpolitik / German Economic Association.
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    36. Qi Li & Juan Lin & Jeffrey S. Racine, 2012. "Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions," Department of Economics Working Papers 2012-10, McMaster University.
    37. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    38. Blaise Melly und Kaspar W thrich, 2016. "Local quantile treatment effects," Diskussionsschriften dp1605, Universitaet Bern, Departement Volkswirtschaft.
    39. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly, 2012. "Inference on counterfactual distributions," CeMMAP working papers 05/12, Institute for Fiscal Studies.
    40. Manuel Arellano & Stéphane Bonhomme, 2017. "Quantile Selection Models With an Application to Understanding Changes in Wage Inequality," Econometrica, Econometric Society, vol. 85, pages 1-28, January.
    41. Patrick A. Adams & Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2020. "Forecasting Macroeconomic Risks," Staff Reports 914, Federal Reserve Bank of New York.
    42. Jonathan Berrisch & Florian Ziel, 2023. "Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Prices," Papers 2303.10019, arXiv.org, revised Feb 2024.
    43. Xing Yan & Weizhong Zhang & Lin Ma & Wei Liu & Qi Wu, 2020. "Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning," Papers 2010.08263, arXiv.org.
    44. Michael Gurkov & Osnat Zohar, 2022. "Growth at Risk: Forecast Distribution of GDP Growth in Israel," Bank of Israel Working Papers 2022.08, Bank of Israel.
    45. Brantly Callaway, 2017. "Job Displacement during the Great Recession: Tight Bounds on Distributional Treatment Effect Parameters using Panel Data," DETU Working Papers 1703, Department of Economics, Temple University.
    46. Samantha Leorato & Franco Peracchi, 2015. "Shape Regressions," EIEF Working Papers Series 1506, Einaudi Institute for Economics and Finance (EIEF), revised Jul 2015.
    47. James Mitchell & Aubrey Poon & Dan Zhu, 2022. "Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics," Working Papers 22-12R, Federal Reserve Bank of Cleveland, revised 11 Apr 2023.
    48. Victor Chernozhukov & Ivan Fernández-Val & Sukjin Han & Amanda Kowalski, 2019. "Censored quantile instrumental-variable estimation with Stata," Stata Journal, StataCorp LP, vol. 19(4), pages 768-781, December.
    49. Julio Galvez & Javier Mencía, 2014. "Distributional Linkages between European Sovereign Bond and Bank Asset Returns," Working Papers wp2014_1407, CEMFI.
    50. Kazi Musa & Norli Ali & Jamaliah Said & Farha Ghapar & Oleg Mariev & Norhayati Mohamed & Hirnissa Mohd Tahir, 2023. "Does the Effectiveness of Budget Deficit Vary between Welfare and Non-Welfare Countries?," Sustainability, MDPI, vol. 15(5), pages 1-22, February.
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    52. Giovanni Bonaccolto & Massimiliano Caporin & Rangan Gupta, 2015. "The Dynamic Impact of Uncertainty in Causing and Forecasting the Distribution of Oil Returns and Risk," Working Papers 201564, University of Pretoria, Department of Economics.
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    56. Roger Koenker & Samantha Leorato & Franco Peracchi, 2013. "Distributional vs. Quantile Regression," CEIS Research Paper 300, Tor Vergata University, CEIS, revised 17 Dec 2013.
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    70. Gabriel Montes-Rojas & Lucas Siga & Ram Mainali, 2017. "Mean and quantile regression Oaxaca-Blinder decompositions with an application to caste discrimination," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 15(3), pages 245-255, September.
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    Cited by:

    1. Candelon, Bertrand & Fuerst, Franz & Hasse, Jean-Baptiste, 2021. "Diversification Potential in Real Estate Portfolios," LIDAM Discussion Papers LFIN 2021001, Université catholique de Louvain, Louvain Finance (LFIN).
    2. Brenda Castillo-Brais & Ángel León & Juan Mora, 2022. "Estimating Value-at-Risk and Expected Shortfall: Do Polynomial Expansions Outperform Parametric Densities?," Mathematics, MDPI, vol. 10(22), pages 1-17, November.
    3. Charles-Olivier Amedee-Manesme & Fabrice Barthélémy, 2012. "Cornish-Fisher expansion for real estate value at risk," ERES eres2012_044, European Real Estate Society (ERES).
    4. Donald Lien & Christopher Stroud & Keying Ye, 2013. "Comparing VaR Approximation Methods Which Use the First Four Moments as Inputs," Working Papers 0220mss, College of Business, University of Texas at San Antonio.
    5. Giuseppe De Luca & Jan R. Magnus & Franco Peracchi, 2019. "Posterior moments and quantiles for the normal location model with Laplace prior," EIEF Working Papers Series 1911, Einaudi Institute for Economics and Finance (EIEF), revised Jun 2019.
    6. Ghossoub, Mario, 2011. "Monotone equimeasurable rearrangements with non-additive probabilities," MPRA Paper 37629, University Library of Munich, Germany, revised 23 Mar 2012.
    7. Liu, Nianqing & Vuong, Quang & Xu, Haiqing, 2017. "Rationalization and identification of binary games with correlated types," Journal of Econometrics, Elsevier, vol. 201(2), pages 249-268.
    8. Charles-Olivier Amédée-Manesme & Fabrice Barthélémy & Didier Maillard, 2017. "Computation of the Corrected Cornish-Fisher Expansion using the Response Surface Methodology: Application to V aR and CV aR," THEMA Working Papers 2017-21, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
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    10. Charles-Olivier Amédée-Manesme & Fabrice Barthélémy, 2022. "Proper use of the modified Sharpe ratios in performance measurement: rearranging the Cornish Fisher expansion," Annals of Operations Research, Springer, vol. 313(2), pages 691-712, June.
    11. Fabrice Barthélémy, 2014. "Cornish-Fisher Expansion for Commercial Real Estate Value at Risk," THEMA Working Papers 2014-29, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
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    13. Stéphane Hamayon & Florence Legros & Pradat Yannick, 2016. "Non gaussian returns: which impact on default options retirement plans? [Distribution non gaussienne des rendements : quel impact sur les options par défaut des plans d'épargne retraite ?]," Working Papers hal-03003588, HAL.
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    Cited by:

    1. Lindqvist, Erik & Östling, Robert, 2006. "Political Polarization and the Size of Government," SSE/EFI Working Paper Series in Economics and Finance 628, Stockholm School of Economics, revised 18 Aug 2009.
    2. Tobias Adrian & Mark M. Westerfield, 2009. "Disagreement and Learning in a Dynamic Contracting Model," The Review of Financial Studies, Society for Financial Studies, vol. 22(10), pages 3873-3906, October.
    3. Triossi, Matteo, 2011. "Games with capacity manipulation : incentives and Nash equilibria," UC3M Working papers. Economics we1125, Universidad Carlos III de Madrid. Departamento de Economía.
    4. Martin W. Cripps & Jeffrey C. Ely & George J. Mailath & Larry Samuelson, 2006. "Common Learning," Cowles Foundation Discussion Papers 1575R, Cowles Foundation for Research in Economics, Yale University, revised Jun 2007.
    5. Lahiri, Kajal & Sheng, Xuguang, 2010. "Learning and heterogeneity in GDP and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 26(2), pages 265-292, April.
    6. Yu, Jialin, 2011. "Disagreement and return predictability of stock portfolios," Journal of Financial Economics, Elsevier, vol. 99(1), pages 162-183, January.
    7. Piotr Evdokimov & Umberto Garfagnini, 2023. "Cognitive Ability and Perceived Disagreement in Learning," Rationality and Competition Discussion Paper Series 381, CRC TRR 190 Rationality and Competition.
    8. Michael J. Lamla & Thomas Maag, 2012. "The Role of Media for Inflation Forecast Disagreement of Households and Professional Forecasters," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(7), pages 1325-1350, October.
    9. Wei Xiong & Hongjun Yan, 2010. "Heterogeneous Expectations and Bond Markets," The Review of Financial Studies, Society for Financial Studies, vol. 23(4), pages 1433-1466, April.
    10. Fudenberg, Drew & Takahashi, Satoru, 2011. "Heterogeneous beliefs and local information in stochastic fictitious play," Games and Economic Behavior, Elsevier, vol. 71(1), pages 100-120, January.
    11. Tim Bollerslev & Jia Li & Yuan Xue, 2016. "Volume, Volatility and Public News Announcements," CREATES Research Papers 2016-19, Department of Economics and Business Economics, Aarhus University.
    12. Jeffrey Hobbs & Hei Wai Lee & Vivek Singh, 2017. "New evidence on the effect of belief heterogeneity on stock returns," Review of Quantitative Finance and Accounting, Springer, vol. 48(2), pages 289-309, February.
    13. Craig Burnside & Martin Eichenbaum & Sergio Rebelo, 2011. "Understanding Booms and Busts in Housing Markets," NBER Working Papers 16734, National Bureau of Economic Research, Inc.
    14. Siklos, Pierre L., 2013. "Sources of disagreement in inflation forecasts: An international empirical investigation," Journal of International Economics, Elsevier, vol. 90(1), pages 218-231.
    15. Jose Alvaro Rodrigues-Neto, 2011. "The Cycles Approach," ANU Working Papers in Economics and Econometrics 2011-547, Australian National University, College of Business and Economics, School of Economics.
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    17. Martin Guzman & Joseph E Stiglitz, 2020. "Towards a dynamic disequilibrium theory with randomness," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 36(3), pages 621-674.
    18. Alexander Zimper & Alexander Ludwig, 2009. "On attitude polarization under Bayesian learning with non-additive beliefs," Journal of Risk and Uncertainty, Springer, vol. 39(2), pages 181-212, October.
    19. David C. Chan, Jr, 2016. "Informational Frictions and Practice Variation: Evidence from Physicians in Training," NBER Working Papers 21855, National Bureau of Economic Research, Inc.
    20. Patton, Andrew J. & Timmermann, Allan, 2010. "Why do forecasters disagree? Lessons from the term structure of cross-sectional dispersion," Journal of Monetary Economics, Elsevier, vol. 57(7), pages 803-820, October.
    21. Maurizio Bovi, 2014. "Shocks and the Expectations Formation Process. A Tale of Two Expectations," Natural Field Experiments 00390, The Field Experiments Website.
    22. Daron Acemoglu & Asuman Ozdaglar, 2011. "Opinion Dynamics and Learning in Social Networks," Dynamic Games and Applications, Springer, vol. 1(1), pages 3-49, March.
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    25. Lena Dräger & Michael J. Lamla, 2015. "Disagreement à la Taylor: Evidence from Survey Microdata," Macroeconomics and Finance Series 201503, University of Hamburg, Department of Socioeconomics.
    26. Alonso, Ricardo & Câmara, Odilon, 2016. "Bayesian persuasion with heterogeneous priors," LSE Research Online Documents on Economics 67950, London School of Economics and Political Science, LSE Library.
    27. Eric Van den Steen, 2010. "Overconfidence by Bayesian Rational Agents," Harvard Business School Working Papers 11-049, Harvard Business School.
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    Cited by:

    1. Holger Dette & Stanislav Volgushev, 2008. "Non‐crossing non‐parametric estimates of quantile curves," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 609-627, July.
    2. Andrew Shephard, 2011. "Equilibrium Search and Tax Credit Reform," Working Papers 1336, Princeton University, Department of Economics, Center for Economic Policy Studies..
    3. Henderson, Daniel J. & Parmeter, Christopher F., 2009. "Imposing Economic Constraints in Nonparametric Regression: Survey, Implementation and Extension," IZA Discussion Papers 4103, Institute of Labor Economics (IZA).
    4. Victor Chernozhukov & Ivan Fernandez-Val & Alfred Galichon, 2009. "Improving point and interval estimators of monotone functions by rearrangement," SciencePo Working papers Main hal-03596970, HAL.
    5. Victor Chernozhukov & Iván Fernández-Val & Alfred Galichon, 2010. "Rearranging Edgeworth–Cornish–Fisher expansions," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 42(2), pages 419-435, February.
    6. Matthew J. Notowidigdo, 2011. "The Incidence of Local Labor Demand Shocks," NBER Working Papers 17167, National Bureau of Economic Research, Inc.
    7. Birke, Melanie, 2008. "Shape constrained kernel density estimation," Technical Reports 2008,08, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    8. Henderson, Daniel J. & List, John A. & Millimet, Daniel L. & Parmeter, Christopher F. & Price, Michael K., 2008. "Imposing Monotonicity Nonparametrically in First-Price Auctions," MPRA Paper 8769, University Library of Munich, Germany.
    9. Birke, Melanie & Bissantz, Nicolai, 2007. "Shape constrained estimators in inverse regression models with convolution-type operator," Technical Reports 2007,35, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

  141. Victor Chernozhukov, 2005. "Extremal quantile regression," Papers math/0505639, arXiv.org.

    Cited by:

    1. Jorge E. Galán, 2020. "The benefits are at the tail: uncovering the impact of macroprudential policy on growth-at-risk," Working Papers 2007, Banco de España.
    2. Chao, Shih-Kang & Härdle, Wolfgang Karl & Yuan, Ming, 2020. "Factorisable Multitask Quantile Regression," IRTG 1792 Discussion Papers 2020-004, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Lee, Ji Hyung, 2016. "Predictive quantile regression with persistent covariates: IVX-QR approach," Journal of Econometrics, Elsevier, vol. 192(1), pages 105-118.
    4. Matthew A Masten & Alexandre Poirier, 2023. "Choosing exogeneity assumptions in potential outcome models," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 327-349.
    5. Eric Blankmeyer, 2012. "Estimating an inflation index by quantile regression," Applied Economics Letters, Taylor & Francis Journals, vol. 19(2), pages 185-187, February.
    6. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2009. "Finite sample inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 152(2), pages 93-103, October.
    7. Habert white & Tae-Hwan Kim & Simone Manganelli, 2012. "VAR for VaR: Measuring Tail Dependence Using Multivariate Regression Quantiles," Working papers 2012rwp-45, Yonsei University, Yonsei Economics Research Institute.
    8. Schimke, Antje, 2014. "Aging workforce and firm growth in the context of "extreme" employment growth events," Working Paper Series in Economics 54, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    9. Lach, Saul & Moraga-González, José-Luis, 2009. "Heterogeneous Price Information and the Effect of Competition," CEPR Discussion Papers 7319, C.E.P.R. Discussion Papers.
    10. Alexandre Belloni & Victor Chernozhukov, 2009. "L1-Penalized quantile regression in high-dimensional sparse models," CeMMAP working papers CWP10/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. López-Espinosa, Germán & Moreno, Antonio & Rubia, Antonio & Valderrama, Laura, 2015. "Systemic risk and asymmetric responses in the financial industry," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 471-485.
    12. Calluzzo, Paul & Dong, Gang Nathan, 2015. "Has the financial system become safer after the crisis? The changing nature of financial institution risk," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 233-248.
    13. Fabrice Etilé, 2009. "Food price policies and the distribution of body mass index: Theory and empirical evidence from France," Working Papers halshs-00586720, HAL.
    14. Hou, Yanxi & Leng, Xuan & Peng, Liang & Zhou, Yinggang, 2024. "Panel quantile regression for extreme risk," Journal of Econometrics, Elsevier, vol. 240(1).
    15. Florens, Jean-Pierre & Simar, Léopold & Van Keilegom, Ingrid, 2014. "Frontier estimation in nonparametric location-scale models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 456-470.
    16. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    17. Daouia, Abdelaati & Stupfler, Gilles & Usseglio-Carleve, Antoine, 2022. "Inference for extremal regression with dependent heavy-tailed data," TSE Working Papers 22-1324, Toulouse School of Economics (TSE), revised 29 Aug 2023.
    18. D’Haultfœuille, Xavier & Maurel, Arnaud & Zhang, Yichong, 2018. "Extremal quantile regressions for selection models and the black–white wage gap," Journal of Econometrics, Elsevier, vol. 203(1), pages 129-142.
    19. Galina Besstremyannaya & Sergei Golovan, 2023. "Measuring heterogeneity in hospital productivity: a quantile regression approach," Journal of Productivity Analysis, Springer, vol. 59(1), pages 15-43, February.
    20. James Mitchell & Aubrey Poon & Dan Zhu, 2022. "Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics," Working Papers 22-12R, Federal Reserve Bank of Cleveland, revised 11 Apr 2023.
    21. Carlos Martins-Filho & Feng Yao & Maximo Torero, 2012. "Nonparametric estimation of conditional value-at-risk and expected shortfall based on extreme value theory," Working Papers 13-05, Department of Economics, West Virginia University.
    22. Sottile, Gianluca & Frumento, Paolo, 2022. "Robust estimation and regression with parametric quantile functions," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    23. Burdekin, Richard C.K. & Siklos, Pierre L., 2022. "Armageddon and the stock market: US, Canadian and Mexican market responses to the 1962 Cuban Missile Crisis," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 112-127.
    24. Dolado, Juan J & Chen, Liang & Gonzalo, Jesus, 2018. "Quantile Factor Models," CEPR Discussion Papers 12716, C.E.P.R. Discussion Papers.
    25. Joseph G. Altonji & Hidehiko Ichimura & Taisuke Otsu, 2008. "Estimating Derivatives in Nonseparable Models with Limited Dependent Variables," Cowles Foundation Discussion Papers 1668R, Cowles Foundation for Research in Economics, Yale University, revised May 2011.
    26. Carlos Martins-Filho & Feng Yao & Maximo Torero, 2015. "High-Order Conditional Quantile Estimation Based on Nonparametric Models of Regression," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 907-958, December.
    27. Stéphane Girard & Gilles Claude Stupfler & Antoine Usseglio-Carleve, 2021. "Extreme Conditional Expectile Estimation in Heavy-Tailed Heteroscedastic Regression Models," Post-Print hal-03306230, HAL.
    28. Saul Lach & Jose Luis Moraga-Gonzalez, 2009. "Asymmetric Price Effects of Competition," Tinbergen Institute Discussion Papers 09-049/2, Tinbergen Institute.
    29. Duschl, Matthias & Schimke, Antje & Brenner, Thomas & Luxen, Dennis, 2011. "Firm growth and the spatial impact of geolocated external factors: Empirical evidence for German manufacturing firms," Working Paper Series in Economics 36, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    30. Benjamin Hamidi & Emmanuel Jurczenko & Bertrand Maillet, 2009. "D'un multiple conditionnel en assurance de portefeuille : CAViaR pour les gestionnaires ?," Post-Print halshs-00389773, HAL.
    31. Philippe Van Kerm & Seunghee Yu & Chung Choe, 2016. "Decomposing quantile wage gaps: a conditional likelihood approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(4), pages 507-527, August.
    32. Besstremyannaya, Galina & Dasher, Richard & Golovan, Sergei, 2022. "Quantifying heterogeneity in the relationship between R&D intensity and growth at innovative Japanese firms: A quantile regression approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 67, pages 27-45.
    33. Denis Chetverikov & Bradley Larsen & Christopher Palmer, 2016. "IV Quantile Regression for Group‐Level Treatments, With an Application to the Distributional Effects of Trade," Econometrica, Econometric Society, vol. 84, pages 809-833, March.
    34. Liao, Wen-Chi & Zhao, Daxuan, 2019. "The selection and quantile treatment effects on the economic returns of green buildings," Regional Science and Urban Economics, Elsevier, vol. 74(C), pages 38-48.
    35. Daisuke Kurisu & Taisuke Otsu, 2021. "Nonparametric inference for extremal conditional quantiles," STICERD - Econometrics Paper Series 616, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    36. Vladislav Morozov, 2022. "Inference on Extreme Quantiles of Unobserved Individual Heterogeneity," Papers 2210.08524, arXiv.org, revised Jun 2023.
    37. Sergey Alexeev, 2023. "Technical change and wage premiums amongst skilled labour: Evidence from the economic transition," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 31(1), pages 189-216, January.
    38. Michal Franta & Jan Vlcek, 2024. "Wage-Price Spirals: A Risk-Based Approach," Working Papers 2024/1, Czech National Bank.
    39. Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Adewuyi, Adeolu O. & Lee, Chien-Chiang, 2022. "Quantile risk spillovers between energy and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak," Energy Economics, Elsevier, vol. 113(C).
    40. Yanbo Liu & Peter C.B. Phillips, 2021. "Robust Inference with Stochastic Local Unit Root Regressors in Predictive Regressions," Cowles Foundation Discussion Papers 2305, Cowles Foundation for Research in Economics, Yale University.
    41. Kurisu, Daisuke & Otsu, Taisuke, 2023. "Subsampling inference for nonparametric extremal conditional quantiles," LSE Research Online Documents on Economics 120365, London School of Economics and Political Science, LSE Library.
    42. Jennifer Betz & Maximilian Nagl & Daniel Rösch, 2022. "Credit line exposure at default modelling using Bayesian mixed effect quantile regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2035-2072, October.
    43. Bousebata, Meryem & Enjolras, Geoffroy & Girard, Stéphane, 2023. "Extreme partial least-squares," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
    44. Lidia Sanchis-Marco & Antonio Rubia Serrano, 2011. "On downside risk predictability through liquidity and trading activity: a quantile regression approach," Working Papers. Serie AD 2011-14, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    45. Joseph Altonji & Hidehiko Ichimura & Taisuke Otsu, 2019. "Nonparametric intermediate order regression quantiles," STICERD - Econometrics Paper Series 608, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    46. Alex Maynard & Katsumi Shimotsu & Nina Kuriyama, 2023. "Inference in Predictive Quantile Regressions," Papers 2306.00296, arXiv.org, revised May 2024.
    47. Qi Zheng & Colin Gallagher & K.B. Kulasekera, 2013. "Adaptively weighted kernel regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(4), pages 855-872, December.
    48. Laura Garcia-Jorcano & Lidia Sanchis-Marco, 2023. "Measuring Systemic Risk Using Multivariate Quantile-Located ES Models," Journal of Financial Econometrics, Oxford University Press, vol. 21(1), pages 1-72.
    49. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    50. Matthias Duschl & Antje Schimke & Thomas Brenner & Dennis Luxen, 2011. "Firm Growth and the Spatial Impact of Geolocated External Factors – Empirical Evidence for German Manufacturing Firms," Working Papers on Innovation and Space 2011-03, Philipps University Marburg, Department of Geography.
    51. Yuya Sasaki & Yulong Wang, 2024. "Extreme Changes in Changes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(2), pages 812-824, April.
    52. Schimke, Antje, 2012. "Entrepreneurial aging and employment growth in the context of extreme growth events," Working Paper Series in Economics 39, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    53. Jerry Hausman & Haoyang Liu & Ye Luo & Christopher Palmer, 2021. "Errors in the Dependent Variable of Quantile Regression Models," Econometrica, Econometric Society, vol. 89(2), pages 849-873, March.
    54. Christis Katsouris, 2023. "Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates," Papers 2302.05193, arXiv.org.
    55. Firpo, Sergio & Galvao, Antonio F. & Pinto, Cristine & Poirier, Alexandre & Sanroman, Graciela, 2022. "GMM quantile regression," Journal of Econometrics, Elsevier, vol. 230(2), pages 432-452.
    56. International Monetary Fund, 2012. "Short-Term Wholesale Funding and Systemic Risk: A Global Covar Approach," IMF Working Papers 2012/046, International Monetary Fund.
    57. Chen, Zhao & Cheng, Vivian Xinyi & Liu, Xu, 2024. "Hypothesis testing on high dimensional quantile regression," Journal of Econometrics, Elsevier, vol. 238(1).
    58. Zernov, Serguei & Zinde-Walsh, Victoria & Galbraith, John W., 2009. "Asymptotics for estimation of quantile regressions with truncated infinite-dimensional processes," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 497-508, March.
    59. Daouia, Abdelaati & Gardes, Laurent & Girard, Stephane, 2013. "On kernel smoothing for extremal quantile regression," LIDAM Reprints ISBA 2013038, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    60. Norman Maswanganyi & Caston Sigauke & Edmore Ranganai, 2021. "Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data," Energies, MDPI, vol. 14(20), pages 1-21, October.
    61. Wei‐han Liu, 2020. "Are Gold and Government Bond Safe‐Haven Assets? An Extremal Quantile Regression Analysis," International Review of Finance, International Review of Finance Ltd., vol. 20(2), pages 451-483, June.
    62. López-Espinosa, Germán & Rubia, Antonio & Valderrama, Laura & Antón, Miguel, 2013. "Good for one, bad for all: Determinants of individual versus systemic risk," Journal of Financial Stability, Elsevier, vol. 9(3), pages 287-299.
    63. Stefan Holst Bache & Christian M. Dahl & Johannes Tang, "undated". "Headlights on tobacco road to low birthweight outcomes - Evidence from a battery of quantile regression estimators and a heterogeneous panelCreation-Date: 20080508," CREATES Research Papers 2008-20, Department of Economics and Business Economics, Aarhus University.
    64. Germán López-Espinosa & Antonio Moreno & Antonio Rubia & Laura Valderrama, 2012. "Short-term Wholesale Funding and Systemic Risk: A Global CoVaR Approach," Faculty Working Papers 02/12, School of Economics and Business Administration, University of Navarra.
    65. Juraj Bodik, 2024. "Extreme Treatment Effect: Extrapolating Dose-Response Function into Extreme Treatment Domain," Mathematics, MDPI, vol. 12(10), pages 1-36, May.
    66. Denis Chetverikov & Yukun Liu & Aleh Tsyvinski, 2022. "Weighted-average quantile regression," Papers 2203.03032, arXiv.org.
    67. Matthias Fischer & Daniel Kraus & Marius Pfeuffer & Claudia Czado, 2017. "Stress Testing German Industry Sectors: Results from a Vine Copula Based Quantile Regression," Risks, MDPI, vol. 5(3), pages 1-13, July.
    68. E. Fusco & R. Benedetti & F. Vidoli, 2023. "Stochastic frontier estimation through parametric modelling of quantile regression coefficients," Empirical Economics, Springer, vol. 64(2), pages 869-896, February.
    69. Patrick Bajari & Han Hong & Minjung Park & Robert Town, 2011. "Regression Discontinuity Designs with an Endogenous Forcing Variable and an Application to Contracting in Health Care," NBER Working Papers 17643, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    2. Arie Beresteanu, 2009. "Sharp Identification Regions in Models with Convex Predictions: Games, Individual Choice, and Incomplete Data," Working Paper 428, Department of Economics, University of Pittsburgh, revised Sep 2010.
    3. Andrew Chesher, 2003. "Nonparametric identification under discrete variation," CeMMAP working papers CWP19/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Molinari, Francesca, 2005. "Missing Treatments," Working Papers 05-11, Cornell University, Center for Analytic Economics.
    5. Donald W.K. Andrews & Patrik Guggenberger, 2007. "Validity of Subsampling and "Plug-in Asymptotic" Inference for Parameters Defined by Moment Inequalities," Cowles Foundation Discussion Papers 1620, Cowles Foundation for Research in Economics, Yale University.
    6. Brent Kreider & John V. Pepper, 2011. "Identification of Expected Outcomes in a Data Error Mixing Model With Multiplicative Mean Independence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 49-60, January.
    7. Wooldridge, Jeffrey M. & Imbens, Guido, 2009. "Recent Developments in the Econometrics of Program Evaluation," Scholarly Articles 3043416, Harvard University Department of Economics.
    8. Patrick Bajari & C. Lanier Benkard & Jonathan Levin, 2007. "Estimating Dynamic Models of Imperfect Competition," Econometrica, Econometric Society, vol. 75(5), pages 1331-1370, September.
    9. Molinari, Francesca, 2008. "Partial identification of probability distributions with misclassified data," Journal of Econometrics, Elsevier, vol. 144(1), pages 81-117, May.
    10. Guido Imbens & Charles F. Manski, 2003. "Confidence intervals for partially identified parameters," CeMMAP working papers CWP09/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Adam Rosen, 2007. "Identification and estimation of firms' marginal cost functions with incomplete knowledge of strategic behavior," CeMMAP working papers CWP03/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Fan, Yanqin & Park, Sang Soo, 2009. "Partial identification of the distribution of treatment effects and its confidence sets," MPRA Paper 37148, University Library of Munich, Germany.
    13. Arie Beresteanu & Francesca Molinari, 2008. "Asymptotic Properties for a Class of Partially Identified Models," Econometrica, Econometric Society, vol. 76(4), pages 763-814, July.
    14. Rosen, Adam M., 2008. "Confidence sets for partially identified parameters that satisfy a finite number of moment inequalities," Journal of Econometrics, Elsevier, vol. 146(1), pages 107-117, September.
    15. J. Stoye, 2009. "Charles F. Manski, Identification for Prediction and Decision (Harvard University Press 2007)," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 857-862.
    16. Schorfheide, Frank & Moon, Hyungsik Roger, 2006. "Boosting Your Instruments: Estimation with Overidentifying Inequality Moment Conditions," CEPR Discussion Papers 5605, C.E.P.R. Discussion Papers.
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    18. Fan, Yanqin & Park, Sang Soo, 2010. "Confidence sets for some partially identified parameters," MPRA Paper 37149, University Library of Munich, Germany.
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    176. Sebastian Bayer & Timo Dimitriadis, 2022. "Regression-Based Expected Shortfall Backtesting [Backtesting Expected Shortfall]," Journal of Financial Econometrics, Oxford University Press, vol. 20(3), pages 437-471.
    177. Takuya Ishihara, 2020. "Panel Data Quantile Regression for Treatment Effect Models," Papers 2001.04324, arXiv.org, revised Nov 2021.
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    179. Ruofan Xu & Jiti Gao & Tatsushi Oka & Yoon-Jae Whang, 2022. "Estimation of Heterogeneous Treatment Effects Using Quantile Regression with Interactive Fixed Effects," Monash Econometrics and Business Statistics Working Papers 13/22, Monash University, Department of Econometrics and Business Statistics.
    180. Escanciano, J.C. & Goh, S.C., 2014. "Specification analysis of linear quantile models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 495-507.
    181. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Inference for high-dimensional sparse econometric models," CeMMAP working papers CWP41/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    182. Manuel Landajo & Javier De Andrés & Pedro Lorca, 2008. "Measuring firm performance by using linear and non‐parametric quantile regressions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(2), pages 227-250, April.
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    186. Taisuke Otsu, 2009. "RESET for quantile regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(2), pages 381-391, August.
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    Cited by:

    1. Booth, Alison & Arulampalam, Wiji & Bryan, Mark L, 2004. "Are There Asymmetries in the Effects of Training on the Conditional Male Wage Distribution?," CEPR Discussion Papers 4289, C.E.P.R. Discussion Papers.
    2. Lembcke, Alexander C. & Kohn, Karsten & Fitzenberger, Bernd, 2008. "Union Density and Varieties of Coverage: The Anatomy of Union Wage Effects in Germany," ZEW Discussion Papers 08-012, ZEW - Leibniz Centre for European Economic Research.
    3. Juergen Jung & Michael Makowsky, 2014. "The determinants of federal and state enforcement of workplace safety regulations: OSHA inspections 1990–2010," Journal of Regulatory Economics, Springer, vol. 45(1), pages 1-33, February.
    4. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
    5. Taisuke Otsu, 2009. "RESET for quantile regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(2), pages 381-391, August.

  145. Victor Chernozhukov & Ivan Fernandez-Val & Amanda E. Kowalski, "undated". "Censored Quantile Instrumental Variable Estimation via Control Functions," Boston University - Department of Economics - Working Papers Series wp2009-012, Boston University - Department of Economics.

    Cited by:

    1. Kwak, Sungil, 2011. "The Impact of Taxes on Charitable Giving: Empirical Evidence from the Korean Labor and Income Panel Study," MPRA Paper 36845, University Library of Munich, Germany.
    2. Pravin K. Trivedi, 2010. "Keynote lecture: Estimation of count-data panel models," Mexican Stata Users' Group Meetings 2010 06, Stata Users Group.
    3. Ubfal, Diego, 2012. "How General Are Time Preferences? Eliciting Good-Specific Discount Rates," IZA Discussion Papers 6774, Institute of Labor Economics (IZA).

  146. V. Chernozhukov & Ivan Fernandez-Val, "undated". "Quantile and Average Effects in Nonseparable Panel Models," Boston University - Department of Economics - Working Papers Series wp2009-011, Boston University - Department of Economics.

    Cited by:

    1. Kato, Kengo & F. Galvao, Antonio & Montes-Rojas, Gabriel V., 2012. "Asymptotics for panel quantile regression models with individual effects," Journal of Econometrics, Elsevier, vol. 170(1), pages 76-91.
    2. Fernando Ubeda & Francisco Pérez-Hernández, 2017. "Absorptive Capacity and Geographical Distance Two Mediating Factors of FDI Spillovers: a Threshold Regression Analysis for Spanish Firms," Journal of Industry, Competition and Trade, Springer, vol. 17(1), pages 1-28, March.
    3. Adam Rosen, 2009. "Set identification via quantile restrictions in short panels," CeMMAP working papers CWP26/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Stefan Hoderlein & Yuya Sasaki, 2011. "On the role of time in nonseparable panel data models," CeMMAP working papers CWP15/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. David Powell, 2010. "Unconditional Quantile Treatment Effects in the Presence of Covariates," Working Papers WR-816, RAND Corporation.
    6. Amaresh Tiwari & Franz Palm, 2011. "Nonlinear Panel Data Models with Expected a Posteriori Values of Correlated Random Effects," CREPP Working Papers 1113, Centre de Recherche en Economie Publique et de la Population (CREPP) (Research Center on Public and Population Economics) HEC-Management School, University of Liège.
    7. Gao, Yichen & Li, Cong & Liang, Zhongwen, 2015. "Binary response correlated random coefficient panel data models," Journal of Econometrics, Elsevier, vol. 188(2), pages 421-434.
    8. Charles F. Manski & John V. Pepper, 2011. "Deterrence and the Death Penalty: Partial Identification Analysis Using Repeated Cross Sections," NBER Working Papers 17455, National Bureau of Economic Research, Inc.
    9. Harding, Matthew & Lamarche, Carlos, 2012. "Estimating and Testing a Quantile Regression Model with Interactive Effects," IZA Discussion Papers 6802, Institute of Labor Economics (IZA).

Articles

  1. Chernozhukov, Victor & Fernández-Val, Iván & Weidner, Martin, 2024. "Network and panel quantile effects via distribution regression," Journal of Econometrics, Elsevier, vol. 240(2).
    See citations under working paper version above.
  2. Victor Chernozhukov & Whitney K. Newey & Andres Santos, 2023. "Constrained Conditional Moment Restriction Models," Econometrica, Econometric Society, vol. 91(2), pages 709-736, March.
    See citations under working paper version above.
  3. V Chernozhukov & W K Newey & R Singh, 2023. "A simple and general debiased machine learning theorem with finite-sample guarantees," Biometrika, Biometrika Trust, vol. 110(1), pages 257-264.
    See citations under working paper version above.
  4. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    See citations under working paper version above.
  5. Guillaume Carlier & Victor Chernozhukov & Gwendoline Bie & Alfred Galichon, 2022. "Correction to: Vector quantile regression and optimal transport, from theory to numerics," Empirical Economics, Springer, vol. 62(1), pages 63-63, January.
    See citations under working paper version above.
  6. Victor Chernozhukov & Whitney K Newey & Rahul Singh, 2022. "Debiased machine learning of global and local parameters using regularized Riesz representers [Semiparametric instrumental variable estimation of treatment response models]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 576-601.
    See citations under working paper version above.
  7. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2022. "Automatic Debiased Machine Learning of Causal and Structural Effects," Econometrica, Econometric Society, vol. 90(3), pages 967-1027, May.
    See citations under working paper version above.
  8. Guillaume Carlier & Victor Chernozhukov & Gwendoline De Bie & Alfred Galichon, 2022. "Vector quantile regression and optimal transport, from theory to numerics," Empirical Economics, Springer, vol. 62(1), pages 35-62, January.
    See citations under working paper version above.
  9. Victor Chernozhukov & Iván Fernández-Val & Blaise Melly, 2022. "Fast algorithms for the quantile regression process," Empirical Economics, Springer, vol. 62(1), pages 7-33, January.
    See citations under working paper version above.
  10. Chernozhukov, Victor & Kasahara, Hiroyuki & Schrimpf, Paul, 2021. "Causal impact of masks, policies, behavior on early covid-19 pandemic in the U.S," Journal of Econometrics, Elsevier, vol. 220(1), pages 23-62.
    See citations under working paper version above.
  11. Daron Acemoglu & Victor Chernozhukov & Iván Werning & Michael D. Whinston, 2021. "Optimal Targeted Lockdowns in a Multigroup SIR Model," American Economic Review: Insights, American Economic Association, vol. 3(4), pages 487-502, December.
    See citations under working paper version above.
  12. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2021. "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1849-1864, October.
    See citations under working paper version above.
  13. Vira Semenova & Victor Chernozhukov, 2021. "Debiased machine learning of conditional average treatment effects and other causal functions," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 264-289.

    Cited by:

    1. Daniel Goller, 2020. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Papers 2008.07165, arXiv.org.
    2. Goller, Daniel & Diem, Andrea & Wolter, Stefan C., 2022. "Sitting Next to a Dropout: Academic Success of Students with More Educated Peers," IZA Discussion Papers 15378, Institute of Labor Economics (IZA).
    3. Vira Semenova & Matt Goldman & Victor Chernozhukov & Matt Taddy, 2023. "Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 471-510, May.
    4. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
    5. Vira Semenova, 2023. "Aggregated Intersection Bounds and Aggregated Minimax Values," Papers 2303.00982, arXiv.org, revised Jun 2024.
    6. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
    7. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
    8. Patrick Rehill & Nicholas Biddle, 2023. "Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability," Papers 2310.13240, arXiv.org, revised Mar 2024.
    9. Yao Cui & Andrew M. Davis, 2022. "Tax-Induced Inequalities in the Sharing Economy," Management Science, INFORMS, vol. 68(10), pages 7202-7220, October.
    10. Patrick Rehill & Nicholas Biddle, 2023. "Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making," Papers 2309.00805, arXiv.org.
    11. Dana Turjeman & Fred M. Feinberg, 2024. "When the Data Are Out: Measuring Behavioral Changes Following a Data Breach," Marketing Science, INFORMS, vol. 43(2), pages 440-461, March.
    12. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    13. Phillip Heiler, 2022. "Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization," Papers 2209.04329, arXiv.org, revised Jul 2024.
    14. Huber, Martin & Meier, Jonas & Wallimann, Hannes, 2022. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 22-39.
    15. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Apr 2024.
    16. Henrika Langen & Martin Huber, 2022. "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign," Papers 2204.10820, arXiv.org, revised Jun 2022.
    17. Tesary Lin & Avner Strulov-Shlain, 2023. "Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data," Papers 2308.13496, arXiv.org.
    18. Nan Liu & Yanbo Liu & Yuya Sasaki, 2024. "Estimation and Inference for Causal Functions with Multiway Clustered Data," Papers 2409.06654, arXiv.org.
    19. Philipp Schwarz & Oliver Schacht & Sven Klaassen & Daniel Grunbaum & Sebastian Imhof & Martin Spindler, 2024. "Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework?," Papers 2406.11308, arXiv.org.
    20. Keisuke Kawata & Mizuki Komura, 2023. "Only-child matching penalty in the marriage market," Discussion Paper Series 254, School of Economics, Kwansei Gakuin University.
    21. Max Vilgalys, 2023. "A Machine Learning Approach to Measuring Climate Adaptation," Papers 2302.01236, arXiv.org.
    22. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    23. Martin Huber & Jannis Kueck, 2022. "Testing the identification of causal effects in observational data," Papers 2203.15890, arXiv.org, revised Jun 2023.
    24. Patrick Rehill, 2024. "How do applied researchers use the Causal Forest? A methodological review of a method," Papers 2404.13356, arXiv.org, revised Dec 2024.
    25. Paul B. Ellickson & Wreetabrata Kar & James C. Reeder, 2023. "Estimating Marketing Component Effects: Double Machine Learning from Targeted Digital Promotions," Marketing Science, INFORMS, vol. 42(4), pages 704-728, July.
    26. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
    27. Kazuhiko Shinoda & Takahiro Hoshino, 2022. "Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions," Papers 2212.13145, arXiv.org.
    28. Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2021. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," Papers 2112.13398, arXiv.org, revised May 2024.
    29. Simon Calmar Andersen & Louise Beuchert & Phillip Heiler & Helena Skyt Nielsen, 2023. "A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health," Papers 2308.04963, arXiv.org.
    30. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.
    31. Riccardo Di Francesco, 2024. "Aggregation Trees," Papers 2410.11408, arXiv.org.
    32. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
    33. Daniel Goller & Andrea Diem & Stefan C. Wolter, 2022. "Sitting next to a dropout: Study success of students with peers that came to the lecture hall by a different route," Economics of Education Working Paper Series 0190, University of Zurich, Department of Business Administration (IBW).
    34. Abdul-Nasah Soale & Emmanuel Selorm Tsyawo, 2023. "Clustered Covariate Regression," Papers 2302.09255, arXiv.org, revised Jul 2023.
    35. Gregory Faletto, 2023. "Fused Extended Two-Way Fixed Effects for Difference-in-Differences With Staggered Adoptions," Papers 2312.05985, arXiv.org, revised Oct 2024.
    36. Adam Baybutt & Manu Navjeevan, 2023. "Doubly-Robust Inference for Conditional Average Treatment Effects with High-Dimensional Controls," Papers 2301.06283, arXiv.org.
    37. Hui Lan & Vasilis Syrgkanis, 2023. "Causal Q-Aggregation for CATE Model Selection," Papers 2310.16945, arXiv.org, revised Nov 2023.
    38. Amilcar Velez, 2024. "On the Asymptotic Properties of Debiased Machine Learning Estimators," Papers 2411.01864, arXiv.org.
    39. Bonev, Petyo & Matsumoto, Shigeru, 2022. "An empirical evaluation of environmental Alternative Dispute Resolution methods," Economics Working Paper Series 2208, University of St. Gallen, School of Economics and Political Science.
    40. Kyle Myers & Wei Yang Tham, 2023. "Money, Time, and Grant Design," Papers 2312.06479, arXiv.org.
    41. Patrick Rehill & Nicholas Biddle, 2024. "Heterogeneous treatment effect estimation with high-dimensional data in public policy evaluation -- an application to the conditioning of cash transfers in Morocco using causal machine learning," Papers 2401.07075, arXiv.org, revised Mar 2024.
    42. Lucas Zhang, 2024. "Continuous difference-in-differences with double/debiased machine learning," Papers 2408.10509, arXiv.org.
    43. Heejun Shin & Joseph Antonelli, 2023. "Improved inference for doubly robust estimators of heterogeneous treatment effects," Biometrics, The International Biometric Society, vol. 79(4), pages 3140-3152, December.

  14. Victor Chernozhukov & Alfred Galichon & Marc Henry & Brendan Pass, 2021. "Identification of Hedonic Equilibrium and Nonseparable Simultaneous Equations," Journal of Political Economy, University of Chicago Press, vol. 129(3), pages 842-870.
    See citations under working paper version above.
  15. Victor Chernozhukov & Hiroyuki Kasahara & Paul Schrimpf, 2021. "The association of opening K–12 schools with the spread of COVID-19 in the United States: County-level panel data analysis," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(42), pages 2103420118-, October.
    See citations under working paper version above.
  16. Victor Chernozhukov & Iván Fernández‐Val & Whitney Newey & Sami Stouli & Francis Vella, 2020. "Semiparametric estimation of structural functions in nonseparable triangular models," Quantitative Economics, Econometric Society, vol. 11(2), pages 503-533, May.
    See citations under working paper version above.
  17. Victor Chernozhukov & Iván Fernández-Val & Blaise Melly & Kaspar Wüthrich, 2020. "Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 123-137, January.
    See citations under working paper version above.
  18. Belloni, Alexandre & Chernozhukov, Victor & Chetverikov, Denis & Fernández-Val, Iván, 2019. "Conditional quantile processes based on series or many regressors," Journal of Econometrics, Elsevier, vol. 213(1), pages 4-29.
    See citations under working paper version above.
  19. Shuowen Chen & Victor Chernozhukov & Iván Fernández-Val, 2019. "Mastering Panel Metrics: Causal Impact of Democracy on Growth," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 77-82, May.
    See citations under working paper version above.
  20. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
    See citations under working paper version above.
  21. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Aureo de Paula, 2019. "Inference on Causal and Structural Parameters using Many Moment Inequalities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(5), pages 1867-1900.
    See citations under working paper version above.
  22. Victor Chernozhukov & Ivan Fernández-Val & Sukjin Han & Amanda Kowalski, 2019. "Censored quantile instrumental-variable estimation with Stata," Stata Journal, StataCorp LP, vol. 19(4), pages 768-781, December.
    See citations under working paper version above.
  23. Patrick Bajari & Victor Chernozhukov & Ali Hortaçsu & Junichi Suzuki, 2019. "The Impact of Big Data on Firm Performance: An Empirical Investigation," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 33-37, May.
    See citations under working paper version above.
  24. Chernozhukov, Victor & Fernández-Val, Iván & Newey, Whitney K., 2019. "Nonseparable multinomial choice models in cross-section and panel data," Journal of Econometrics, Elsevier, vol. 211(1), pages 104-116.
    See citations under working paper version above.
  25. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    See citations under working paper version above.
  26. Victor Chernozhukov & Iván Fernández‐Val & Ye Luo, 2018. "The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages," Econometrica, Econometric Society, vol. 86(6), pages 1911-1938, November.
    See citations under working paper version above.
  27. Carlier, Guillaume & Chernozhukov, Victor & Galichon, Alfred, 2017. "Vector quantile regression beyond the specified case," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 96-102.

    Cited by:

    1. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," SciencePo Working papers Main hal-03936221, HAL.
    2. Guillaume Carlier & Victor Chernozhukov & Gwendoline De Bie & Alfred Galichon, 2022. "Vector quantile regression and optimal transport, from theory to numerics," Empirical Economics, Springer, vol. 62(1), pages 35-62, January.
    3. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Ozdemir, Huseyin & Wohar, Mark E., 2020. "Transmission of US and EU Economic Policy Uncertainty Shock to Asian Economies in Bad and Good Times," IZA Discussion Papers 13274, Institute of Labor Economics (IZA).
    4. Marc Hallin, 2021. "Measure Transportation and Statistical Decision Theory," Working Papers ECARES 2021-04, ULB -- Universite Libre de Bruxelles.
    5. Nadja Klein & Torsten Hothorn & Luisa Barbanti & Thomas Kneib, 2022. "Multivariate conditional transformation models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 116-142, March.
    6. Hongjian Shi & Mathias Drton & Marc Hallin & Fang Han, 2023. "Semiparametrically Efficient Tests of Multivariate Independence Using Center-Outward Quadrant, Spearman, and Kendall Statistics," Working Papers ECARES 2023-03, ULB -- Universite Libre de Bruxelles.
    7. Donald Lien & Zijun Wang, 2019. "Quantile information share," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(1), pages 38-55, January.
    8. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Working Papers hal-03936221, HAL.
    9. Alfred Galichon, 2021. "The unreasonable effectiveness of optimal transport in economics," Papers 2107.04700, arXiv.org.

  28. A. Belloni & V. Chernozhukov & I. Fernández‐Val & C. Hansen, 2017. "Program Evaluation and Causal Inference With High‐Dimensional Data," Econometrica, Econometric Society, vol. 85, pages 233-298, January.
    See citations under working paper version above.
  29. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey, 2017. "Double/Debiased/Neyman Machine Learning of Treatment Effects," American Economic Review, American Economic Association, vol. 107(5), pages 261-265, May.

    Cited by:

    1. TELLO, Mario D., 2024. "Inversión Pública En Infraestructura Y Crecimiento Regional En Perú, 2005-2020: Un Análisis Basado En Técnicas De Aprendizaje Automático Causal," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 24(2), pages 195-222.
    2. Xing, Lu & Han, DongHao & Hui, Xie, 2023. "The impact of carbon policy on corporate risk-taking with a double/debiased machine learning based difference-in-differences approach," Finance Research Letters, Elsevier, vol. 58(PC).
    3. Delprato, Marcos & Frola, Alessia & Antequera, Germán, 2022. "Indigenous and non-Indigenous proficiency gaps for out-of-school and in-school populations: A machine learning approach," International Journal of Educational Development, Elsevier, vol. 93(C).
    4. Pedro H. C. Sant'Anna & Jun B. Zhao, 2018. "Doubly Robust Difference-in-Differences Estimators," Papers 1812.01723, arXiv.org, revised May 2020.
    5. Xiong, Ruoxuan & Koenecke, Allison & Powell, Michael & Shen, Zhu & Vogelstein, Joshua T. & Athey, Susan, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Research Papers 3990, Stanford University, Graduate School of Business.
    6. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Tinbergen Institute Discussion Papers 21-001/V, Tinbergen Institute.
    7. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org, revised Nov 2024.
    8. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).
    9. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2020. "Unconditional Quantile Regression with High Dimensional Data," Papers 2007.13659, arXiv.org, revised Feb 2022.
    10. Maximilian Maurice Gail & Phil-Adrian Klotz, 2021. "The Impact of the Agency Model on E-book Prices: Evidence from the UK," MAGKS Papers on Economics 202111, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    11. Oliver Hines & Stijn Vansteelandt & Karla Diaz-Ordaz, 2021. "Robust Inference for Mediated Effects in Partially Linear Models," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 595-618, June.
    12. Jong Hee Park & Byung Koo Kim, 2020. "Why your neighbor matters: Positions in preferential trade agreement networks and export growth in global value chains," Economics and Politics, Wiley Blackwell, vol. 32(3), pages 381-410, November.
    13. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
    14. Vira Semenova, 2017. "Debiased Machine Learning of Set-Identified Linear Models," Papers 1712.10024, arXiv.org, revised Dec 2022.
    15. Ziwei Cong & Jia Liu & Puneet Manchanda, 2021. "The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest," Papers 2107.01629, arXiv.org, revised Sep 2022.
    16. Amit Sharma & Emre Kiciman, 2020. "DoWhy: An End-to-End Library for Causal Inference," Papers 2011.04216, arXiv.org.
    17. Feng, Sanying & Kong, Kaidi & Kong, Yinfei & Li, Gaorong & Wang, Zhaoliang, 2022. "Statistical inference of heterogeneous treatment effect based on single-index model," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    18. Susan Athey & Guido Imbens & Jonas Metzger & Evan Munro, 2019. "Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations," Papers 1909.02210, arXiv.org, revised Jul 2020.
    19. Miquel Oliu-Barton & Bary S R Pradelski & Nicolas Woloszko & Lionel Guetta-Jeanrenaud & Philippe Aghion & Patrick Artus & Arnaud Fontanet & Philippe Martin & Guntram B Wolff, 2022. "The Effect of COVID Certificates on Vaccine Uptake, Health Outcomes, and the Economy," Post-Print hal-03813557, HAL.
    20. Victor Chernozhukov & Whitney Newey & Vira Semenova, 2019. "Welfare Analysis in Dynamic Models," Papers 1908.09173, arXiv.org, revised Nov 2024.
    21. Waverly Wei & Maya Petersen & Mark J van der Laan & Zeyu Zheng & Chong Wu & Jingshen Wang, 2023. "Efficient targeted learning of heterogeneous treatment effects for multiple subgroups," Biometrics, The International Biometric Society, vol. 79(3), pages 1934-1946, September.
    22. Jinyong Hahn & Jerry Hausman, 2021. "Problems with the Control Variable Approach in Achieving Unbiased Estimates in Nonlinear Models in the Presence of Many Instruments," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 39-58, December.
    23. Sven Klaassen & Jannis Kuck & Martin Spindler & Victor Chernozhukov, 2018. "Uniform Inference in High-Dimensional Gaussian Graphical Models," Papers 1808.10532, arXiv.org, revised Dec 2018.
    24. Wang, Hongning & Ma, Sanjun, 2022. "Preventing crimes against public health with artificial intelligence and machine learning capabilities," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    25. Yong Bian & Xiqian Wang & Qin Zhang, 2023. "How Does China's Household Portfolio Selection Vary with Financial Inclusion?," Papers 2311.01206, arXiv.org.
    26. Betts,Alexander Milton Stedman & Stierna,Maria Flinder & Omata,Naohiko & Sterck,Olivier Christian Brigitte, 2022. "Social Cohesion and Refugee-Host Interactions : Evidence from East Africa," Policy Research Working Paper Series 9917, The World Bank.
    27. Monica Andini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Viola Salvestrini, 2017. "Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy," Temi di discussione (Economic working papers) 1158, Bank of Italy, Economic Research and International Relations Area.
    28. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
    29. Bobonis, Gustavo & Gertler, Paul & Gonzalez-Navarro, Marco & Nichter, Simeon, 2023. "Does Combating Corruption Reduce Clientelism?," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt13k514pd, Department of Agricultural & Resource Economics, UC Berkeley.
    30. Jules Gazeaud & Nausheen Khan & Eric Mvukiyehe & Olivier Sterck, 2023. "With or without him? Experimental evidence on cash grants and gender-sensitive trainings in Tunisia," Post-Print hal-04364356, HAL.
    31. Sven Klaassen & Jannis Kueck & Martin Spindler, 2017. "Transformation Models in High-Dimensions," Papers 1712.07364, arXiv.org.
    32. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
    33. Jikai Jin & Vasilis Syrgkanis, 2024. "Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation," Papers 2402.14264, arXiv.org, revised Mar 2024.
    34. Francesco Decarolis & Cristina Giorgiantonio, 2020. "Corruption red flags in public procurement: new evidence from Italian calls for tenders," Questioni di Economia e Finanza (Occasional Papers) 544, Bank of Italy, Economic Research and International Relations Area.
    35. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2023. "Source Condition Double Robust Inference on Functionals of Inverse Problems," Papers 2307.13793, arXiv.org.
    36. Vira Semenova, 2018. "Machine Learning for Dynamic Discrete Choice," Papers 1808.02569, arXiv.org, revised Nov 2018.
    37. Carl Bonander & Mikael Svensson, 2021. "Using causal forests to assess heterogeneity in cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 30(8), pages 1818-1832, August.
    38. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    39. Huber, Martin & Wüthrich, Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," University of California at San Diego, Economics Working Paper Series qt4j29d8sc, Department of Economics, UC San Diego.
    40. Valente, Marica, 2023. "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
    41. Maria Cuellar & Edward H. Kennedy, 2020. "A non‐parametric projection‐based estimator for the probability of causation, with application to water sanitation in Kenya," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1793-1818, October.
    42. Paolo Libenzio Brignoli & Alessandro Varacca & Cornelis Gardebroek & Paolo Sckokai, 2024. "Machine learning to predict grains futures prices," Agricultural Economics, International Association of Agricultural Economists, vol. 55(3), pages 479-497, May.
    43. Laura Derksen & Jason Kerwin & Natalia Ordaz Reynoso & Olivier Sterck, 2021. "Appointments: A More Effective Commitment Device for Health Behaviors," Papers 2110.06876, arXiv.org.
    44. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Papers 2101.00878, arXiv.org.
    45. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2017. "Non-separable Models with High-dimensional Data," Economics and Statistics Working Papers 15-2017, Singapore Management University, School of Economics.
    46. Andres Algaba & David Ardia & Keven Bluteau & Samuel Borms & Kris Boudt, 2020. "Econometrics Meets Sentiment: An Overview Of Methodology And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 34(3), pages 512-547, July.
    47. Anna Baiardi & Andrea A Naghi, 2024. "The value added of machine learning to causal inference: evidence from revisited studies," The Econometrics Journal, Royal Economic Society, vol. 27(2), pages 213-234.
    48. Zhen Li & Jie Chen & Eric Laber & Fang Liu & Richard Baumgartner, 2023. "Optimal Treatment Regimes: A Review and Empirical Comparison," International Statistical Review, International Statistical Institute, vol. 91(3), pages 427-463, December.
    49. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    50. Whitney K. Newey & James M. Robins, 2017. "Cross-fitting and fast remainder rates for semiparametric estimation," CeMMAP working papers CWP41/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    51. Julia Hatamyar & Noemi Kreif, 2023. "Policy Learning with Rare Outcomes," Papers 2302.05260, arXiv.org, revised Oct 2023.
    52. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    53. Wang, Xiqian & Bian, Yong & Zhang, Qin, 2023. "The effect of cooking fuel choice on the elderly’s well-being: Evidence from two non-parametric methods," Energy Economics, Elsevier, vol. 125(C).
    54. Elliott Ash & Daniel L. Chen & Sergio Galletta, 2022. "Measuring Judicial Sentiment: Methods and Application to US Circuit Courts," Economica, London School of Economics and Political Science, vol. 89(354), pages 362-376, April.
    55. Heiler, Phillip & Kazak, Ekaterina, 2021. "Valid inference for treatment effect parameters under irregular identification and many extreme propensity scores," Journal of Econometrics, Elsevier, vol. 222(2), pages 1083-1108.
    56. Pradhi Aggarwal & Alec Brandon & Ariel Goldszmidt & Justin Holz & John List & Ian Muir & Gregory Sun & Thomas Yu, 2022. "High-frequency location data shows that race affects the likelihood of being stopped and fined for speeding," Natural Field Experiments 00764, The Field Experiments Website.
    57. Songul Cinaroglu, 2020. "Modelling unbalanced catastrophic health expenditure data by using machine‐learning methods," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(4), pages 168-181, October.
    58. Jiaming Mao & Zhesheng Zheng, 2020. "Structural Regularization," Papers 2004.12601, arXiv.org, revised Jun 2020.
    59. Matt Goldman & Brian Quistorff, 2018. "Pricing Engine: Estimating Causal Impacts in Real World Business Settings," Papers 1806.03285, arXiv.org, revised Jun 2018.
    60. Nikolaos Ignatiadis & Wolfgang Huber, 2021. "Covariate powered cross‐weighted multiple testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 720-751, September.
    61. Miruna Oprescu & Vasilis Syrgkanis & Zhiwei Steven Wu, 2018. "Orthogonal Random Forest for Causal Inference," Papers 1806.03467, arXiv.org, revised Sep 2019.
    62. Gareth Liu-Evans & Shalini Mitra, 2023. "Formal sector enforcement and welfare," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 30(3), pages 706-728, June.
    63. Jiaming Mao & Jingzhi Xu, 2020. "Ensemble Learning with Statistical and Structural Models," Papers 2006.05308, arXiv.org.
    64. Mochen Yang & Edward McFowland & Gordon Burtch & Gediminas Adomavicius, 2022. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 138-155, October.
    65. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    66. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," THEMA Working Papers 2024-01, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    67. Fernando Delbianco & Fernando Tohmé, 2023. "Individualized Conformal," Working Papers 247, Red Nacional de Investigadores en Economía (RedNIE).
    68. Sander Gerritsen & Mark Kattenberg & Sonny Kuijpers, 2019. "The impact of age at arrival on education and mental health," CPB Discussion Paper 389, CPB Netherlands Bureau for Economic Policy Analysis.
    69. Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2021. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," Papers 2112.13398, arXiv.org, revised May 2024.
    70. Fukuyama, Hirofumi & Tsionas, Mike & Tan, Yong, 2023. "Dynamic network data envelopment analysis with a sequential structure and behavioural-causal analysis: Application to the Chinese banking industry," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1360-1373.
    71. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.
    72. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    73. Jonas Metzger, 2022. "Adversarial Estimators," Papers 2204.10495, arXiv.org, revised Jun 2022.
    74. Max Cytrynbaum, 2021. "Optimal Stratification of Survey Experiments," Papers 2111.08157, arXiv.org, revised Aug 2023.
    75. Dongcheng Zhang & Kunpeng Zhang, 2020. "Weighting-Based Treatment Effect Estimation via Distribution Learning," Papers 2012.13805, arXiv.org, revised May 2023.
    76. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
    77. Alexander Buchholz & Vito Bellini & Giuseppe Di Benedetto & Yannik Stein & Matteo Ruffini & Fabian Moerchen, 2022. "Fair Effect Attribution in Parallel Online Experiments," Papers 2210.08338, arXiv.org.
    78. Andreas Fuster & Paul Goldsmith‐Pinkham & Tarun Ramadorai & Ansgar Walther, 2022. "Predictably Unequal? The Effects of Machine Learning on Credit Markets," Journal of Finance, American Finance Association, vol. 77(1), pages 5-47, February.
    79. Duncan Simester & Artem Timoshenko & Spyros I. Zoumpoulis, 2020. "Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges," Management Science, INFORMS, vol. 66(6), pages 2495-2522, June.
    80. Mei, Ziwei & Shi, Zhentao, 2024. "On LASSO for high dimensional predictive regression," Journal of Econometrics, Elsevier, vol. 242(2).
    81. Heigle, Julia & Pfeiffer, Friedhelm, 2019. "An analysis of selected labor market outcomes of college dropouts in Germany: A machine learning estimation approach. Research report," ZEW Expertises, ZEW - Leibniz Centre for European Economic Research, number 222378.
    82. Krantz, Sebastian, 2024. "Mapping Africa's infrastructure potential with geospatial big data and causal ML," Kiel Working Papers 2276, Kiel Institute for the World Economy (IfW Kiel).
    83. Stephan Martin, 2022. "Estimation of Conditional Random Coefficient Models using Machine Learning Techniques," Papers 2201.08366, arXiv.org.
    84. Belloni, Alexandre & Hansen, Christian & Newey, Whitney, 2022. "High-dimensional linear models with many endogenous variables," Journal of Econometrics, Elsevier, vol. 228(1), pages 4-26.
    85. Falco J. Bargagli Stoffi & Kenneth De Beckker & Joana E. Maldonado & Kristof De Witte, 2021. "Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy," Papers 2102.04382, arXiv.org.
    86. Ashkan Ertefaie & Nima S. Hejazi & Mark J. van der Laan, 2023. "Nonparametric inverse‐probability‐weighted estimators based on the highly adaptive lasso," Biometrics, The International Biometric Society, vol. 79(2), pages 1029-1041, June.
    87. Guber, Raphael, 2018. "Instrument Validity Tests with Causal Trees: With an Application to the Same-sex Instrument," MEA discussion paper series 201805, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    88. Jushan Bai & Sung Hoon Choi & Yuan Liao, 2019. "Feasible Generalized Least Squares for Panel Data with Cross-sectional and Serial Correlations," Papers 1910.09004, arXiv.org, revised Aug 2020.
    89. Esfandiar Maasoumi & Jianqiu Wang & Zhuo Wang & Ke Wu, 2024. "Identifying factors via automatic debiased machine learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 438-461, April.
    90. Retsef Levi & Elisabeth Paulson & Georgia Perakis & Emily Zhang, 2024. "Heterogeneous Treatment Effects in Panel Data," Papers 2406.05633, arXiv.org.
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    93. Phillip Heiler, 2020. "Efficient Covariate Balancing for the Local Average Treatment Effect," Papers 2007.04346, arXiv.org.
    94. Ke Sun & Linglong Kong & Hongtu Zhu & Chengchun Shi, 2024. "ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments," Papers 2408.05342, arXiv.org, revised Dec 2024.
    95. Whitney K. Newey & James M. Robins, 2017. "Cross-fitting and fast remainder rates for semiparametric estimation," CeMMAP working papers 41/17, Institute for Fiscal Studies.
    96. Johannes Jakubik & Stefan Feuerriegel, 2022. "Data‐driven allocation of development aid toward sustainable development goals: Evidence from HIV/AIDS," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2739-2756, June.

  30. , & , & ,, 2016. "Fragility of asymptotic agreement under Bayesian learning," Theoretical Economics, Econometric Society, vol. 11(1), January.
    See citations under working paper version above.
  31. Alexandre Belloni & Victor Chernozhukov & Christian Hansen & Damian Kozbur, 2016. "Inference in High-Dimensional Panel Models With an Application to Gun Control," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 590-605, October.
    See citations under working paper version above.
  32. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2016. "Post-Selection Inference for Generalized Linear Models With Many Controls," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 606-619, October.
    See citations under working paper version above.
  33. Chernozhukov, Victor & Chetverikov, Denis & Kato, Kengo, 2016. "Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings," Stochastic Processes and their Applications, Elsevier, vol. 126(12), pages 3632-3651.
    See citations under working paper version above.
  34. Chernozhukov, Victor & Fernández-Val, Iván & Kowalski, Amanda E., 2015. "Quantile regression with censoring and endogeneity," Journal of Econometrics, Elsevier, vol. 186(1), pages 201-221.
    See citations under working paper version above.
  35. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," American Economic Review, American Economic Association, vol. 105(5), pages 486-490, May.
    See citations under working paper version above.
  36. Chernozhukov, Victor & Fernández-Val, Iván & Hoderlein, Stefan & Holzmann, Hajo & Newey, Whitney, 2015. "Nonparametric identification in panels using quantiles," Journal of Econometrics, Elsevier, vol. 188(2), pages 378-392.
    See citations under working paper version above.
  37. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 649-688, August.
    See citations under working paper version above.
  38. Victor Chernozhukov & Emre Kocatulum & Konrad Menzel, 2015. "Inference on sets in finance," Quantitative Economics, Econometric Society, vol. 6(2), pages 309-358, July.
    See citations under working paper version above.
  39. Belloni, Alexandre & Chernozhukov, Victor & Chetverikov, Denis & Kato, Kengo, 2015. "Some new asymptotic theory for least squares series: Pointwise and uniform results," Journal of Econometrics, Elsevier, vol. 186(2), pages 345-366.
    See citations under working paper version above.
  40. Victor Chernozhukov & Wooyoung Kim & Sokbae Lee & Adam M. Rosen, 2015. "Implementing intersection bounds in Stata," Stata Journal, StataCorp LP, vol. 15(1), pages 21-44, March.
    See citations under working paper version above.
  41. A. Belloni & V. Chernozhukov & K. Kato, 2015. "Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems," Biometrika, Biometrika Trust, vol. 102(1), pages 77-94.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    3. Jelena Bradic & Weijie Ji & Yuqian Zhang, 2021. "High-dimensional Inference for Dynamic Treatment Effects," Papers 2110.04924, arXiv.org, revised May 2023.
    4. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    5. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    7. Victor Chernozhukov & Chen Huang & Weining Wang, 2021. "Uniform Inference on High-dimensional Spatial Panel Networks," Papers 2105.07424, arXiv.org, revised Sep 2023.
    8. Yukun Ma, 2023. "Identification-robust inference for the LATE with high-dimensional covariates," Papers 2302.09756, arXiv.org, revised Nov 2023.
    9. Han, Dongxiao & Huang, Jian & Lin, Yuanyuan & Shen, Guohao, 2022. "Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 230(2), pages 416-431.
    10. Victor Chernozhukov & Whitney Newey & Rahul Singh, 2018. "De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers," Papers 1802.08667, arXiv.org, revised Oct 2022.
    11. Ekaterina Seregina, 2020. "A Basket Half Full: Sparse Portfolios," Papers 2011.04278, arXiv.org, revised Apr 2021.
    12. Jelena Bradic & Victor Chernozhukov & Whitney K. Newey & Yinchu Zhu, 2019. "Minimax Semiparametric Learning With Approximate Sparsity," Papers 1912.12213, arXiv.org, revised Aug 2022.
    13. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.
    14. Harold D. Chiang, 2019. "Many Average Partial Effects: with an Application to Text Regression," 2019 Papers pch1836, Job Market Papers.
    15. Harold D Chiang & Yukun Ma & Joel Rodrigue & Yuya Sasaki, 2021. "Dyadic double/debiased machine learning for analyzing determinants of free trade agreements," Papers 2110.04365, arXiv.org, revised Dec 2022.
    16. Lina Liao & Cheolwoo Park & Hosik Choi, 2019. "Penalized expectile regression: an alternative to penalized quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(2), pages 409-438, April.
    17. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2022. "High-dimensional Data Bootstrap," Papers 2205.09691, arXiv.org.
    18. Roger Koenker, 2017. "Quantile regression 40 years on," CeMMAP working papers CWP36/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. Harold D. Chiang & Kengo Kato & Yukun Ma & Yuya Sasaki, 2019. "Multiway Cluster Robust Double/Debiased Machine Learning," Papers 1909.03489, arXiv.org, revised Mar 2020.
    20. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    21. van de Geer, Sara, 2016. "Worst possible sub-directions in high-dimensional models," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 248-260.
    22. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    23. Roger Koenker, 2017. "Quantile regression 40 years on," CeMMAP working papers 36/17, Institute for Fiscal Studies.
    24. Ali Charkhi & Gerda Claeskens, 2018. "Asymptotic post-selection inference for the Akaike information criterion," Biometrika, Biometrika Trust, vol. 105(3), pages 645-664.
    25. Georg Keilbar & Juan M. Rodriguez-Poo & Alexandra Soberon & Weining Wang, 2022. "A semiparametric approach for interactive fixed effects panel data models," Papers 2201.11482, arXiv.org, revised Mar 2023.
    26. Philipp Bach & Victor Chernozhukov & Martin Spindler, 2019. "Valid simultaneous inference in high-dimensional settings (with the HDM package for R)," CeMMAP working papers CWP30/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    27. Victor Chernozhukov & Whitney K Newey & Rahul Singh, 2018. "Automatic Debiased Machine Learning of Causal and Structural Effects," Papers 1809.05224, arXiv.org, revised Oct 2022.
    28. Jooyoung Cha & Harold D. Chiang & Yuya Sasaki, 2021. "Inference in high-dimensional regression models without the exact or $L^p$ sparsity," Papers 2108.09520, arXiv.org, revised Dec 2022.
    29. Victor Chernozhukov & Whitney K. Newey & James Robins, 2018. "Double/de-biased machine learning using regularized Riesz representers," CeMMAP working papers CWP15/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    30. Algo Carè & Simone Garatti & Marco C. Campi, 2017. "A coverage theory for least squares," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1367-1389, November.
    31. Dai, Wei & Tsang, Ka Wai, 2023. "A resampling approach for confidence intervals in linear time-series models after model selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).

  42. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "Inference on Treatment Effects after Selection among High-Dimensional Controlsâ€," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(2), pages 608-650.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    3. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
    4. Achim Ahrens & Sean Lyons, 2021. "Do rising rents lead to longer commutes? A gravity model of commuting flows in Ireland," Urban Studies, Urban Studies Journal Limited, vol. 58(2), pages 264-279, February.
    5. Joel L. Horowitz & Ahnaf Rafi, 2023. "Bootstrap based asymptotic refinements for high-dimensional nonlinear models," Papers 2303.09680, arXiv.org, revised Feb 2024.
    6. Caner, Mehmet & Kock, Anders Bredahl, 2018. "Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso," Journal of Econometrics, Elsevier, vol. 203(1), pages 143-168.
    7. TELLO, Mario D., 2024. "Inversión Pública En Infraestructura Y Crecimiento Regional En Perú, 2005-2020: Un Análisis Basado En Técnicas De Aprendizaje Automático Causal," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 24(2), pages 195-222.
    8. Abhijit Banerjee & Esther Duflo & Amy Finkelstein & Lawrence F. Katz & Benjamin A. Olken & Anja Sautmann, 2020. "In Praise of Moderation: Suggestions for the Scope and Use of Pre-Analysis Plans for RCTs in Economics," NBER Working Papers 26993, National Bureau of Economic Research, Inc.
    9. Georgia Papadogeorgou, 2022. "Discussion on “Spatial+: a novel approach to spatial confounding” by Emiko Dupont, Simon N. Wood, and Nicole H. Augustin," Biometrics, The International Biometric Society, vol. 78(4), pages 1305-1308, December.
    10. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Julián Caballero & Christian Upper, 2023. "What happens to EMEs when US yields go up?," BIS Working Papers 1081, Bank for International Settlements.
    12. Zhong, Wei & Gao, Yang & Zhou, Wei & Fan, Qingliang, 2021. "Endogenous treatment effect estimation using high-dimensional instruments and double selection," Statistics & Probability Letters, Elsevier, vol. 169(C).
    13. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, 2016. "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
    14. Neng-Chieh Chang, 2020. "The Mode Treatment Effect," Papers 2007.11606, arXiv.org.
    15. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    16. Meles, Tensay Hadush & Lokina, Razack & Mtenga, Erica Louis & Tibanywana, Julieth Julius, 2023. "Stated preferences with survey consequentiality and outcome uncertainty: A split sample discrete choice experiment," EfD Discussion Paper 23-16, Environment for Development, University of Gothenburg.
    17. Pedro H. C. Sant'Anna & Jun B. Zhao, 2018. "Doubly Robust Difference-in-Differences Estimators," Papers 1812.01723, arXiv.org, revised May 2020.
    18. Micevska, Maja, 2021. "Revisiting forced migration: A machine learning perspective," European Journal of Political Economy, Elsevier, vol. 70(C).
    19. Orazio Attanasio & Richard Blundell & Gabriella Conti & Giacomo Mason, 2018. "Inequality in Socio-emotional Skills: A Cross-Cohort Comparison," NBER Chapters, in: Inequality and Public Policy, Trans-Atlantic Public Economics Seminar 2018, National Bureau of Economic Research, Inc.
    20. Kaspar Wuthrich & Ying Zhu, 2019. "Omitted variable bias of Lasso-based inference methods: A finite sample analysis," Papers 1903.08704, arXiv.org, revised Sep 2021.
    21. Huber, Martin, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    22. Elyasiani, Elyas & Movaghari, Hadi, 2022. "Determinants of corporate cash holdings: An application of a robust variable selection technique," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 967-993.
    23. Battaglini, Marco & Crawford, Forrest & Patacchini, Eleonora & Peng, Sida, 2020. "A Graphical Lasso Approach to Estimating Network Connections: The Case of U.S. Lawmakers," CEPR Discussion Papers 15041, C.E.P.R. Discussion Papers.
    24. Davies,Elwyn Adriaan Robin & Deffebach,Peter William & Iacovone,Leonardo & Mckenzie,David J., 2023. "Training Microentrepreneurs over Zoom : Experimental Evidence from Mexico," Policy Research Working Paper Series 10574, The World Bank.
    25. Backes-Gellner, Uschi & Herz, Holger & Kosfeld, Michael & Oswald, Yvonne, 2018. "Do Preferences and Biases Predict Life Outcomes? Evidence from Education and Labor Market Entry Decisions," IZA Discussion Papers 11288, Institute of Labor Economics (IZA).
    26. Victor Chernozhukov & Mert Demirer & Esther Duflo & Ivan Fernandez-Val, 2017. "Generic machine learning inference on heterogenous treatment effects in randomized experiments," CeMMAP working papers CWP61/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    27. Fremstad, Anders, 2017. "Does Craigslist Reduce Waste? Evidence from California and Florida," Ecological Economics, Elsevier, vol. 132(C), pages 135-143.
    28. Olaf J de Groot & Carlos Bozzoli & Anousheh Alamir & Tilman Brück, 2022. "The global economic burden of violent conflict," Journal of Peace Research, Peace Research Institute Oslo, vol. 59(2), pages 259-276, March.
    29. Vira Semenova & Matt Goldman & Victor Chernozhukov & Matt Taddy, 2023. "Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 471-510, May.
    30. Franz Huber & Alan Ponce & Francesco Rentocchini & Thomas Wainwright, 2020. "The Wealth of (Open Data) Nations? Examining the interplay of open government data and country-level institutions for entrepreneurial activity at the country-level," SEEDS Working Papers 1120, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Nov 2020.
    31. Khanh Duong, 2024. "Is meritocracy just? New evidence from Boolean analysis and Machine learning," Journal of Computational Social Science, Springer, vol. 7(2), pages 1795-1821, October.
    32. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    33. Bellmann, Lutz & Hübler, Olaf, 2019. "Personal Attitudes, Job Characteristics and Health," IZA Discussion Papers 12597, Institute of Labor Economics (IZA).
    34. Janzen, Sarah A. & Magnan, Nicholas & Mullally, Conner C. & Sharma, Shruti, 2021. "Training and Shifting Gender Norms: Evidence from a training intervention in rural Nepal," 2021 Annual Meeting, August 1-3, Austin, Texas 314065, Agricultural and Applied Economics Association.
    35. Peter C. B. Phillips & Zhentao Shi, 2021. "Boosting: Why You Can Use The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 521-570, May.
    36. Anaka Aiyar & Naveen Sunder, 2024. "Health insurance and child mortality: Evidence from India," Health Economics, John Wiley & Sons, Ltd., vol. 33(5), pages 870-893, May.
    37. Andrew B. Martinez, 2020. "Forecast Accuracy Matters for Hurricane Damages," Working Papers 2020-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    38. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2024. "ddml: Double/debiased machine learning in Stata," Stata Journal, StataCorp LP, vol. 24(1), pages 3-45, March.
    39. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    40. Qingliang Fan & Zijian Guo & Ziwei Mei & Cun-Hui Zhang, 2023. "Inference for Nonlinear Endogenous Treatment Effects Accounting for High-Dimensional Covariate Complexity," Papers 2310.08063, arXiv.org, revised Jun 2024.
    41. Harrison Fell & Melinda Sandler Morrill, 2024. "The Impact of Wind Energy on Air Pollution and Emergency Department Visits," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(1), pages 287-320, January.
    42. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    43. Byunghoon Kang, 2017. "Inference in Nonparametric Series Estimation with Data-Dependent Undersmoothing," Working Papers 170712442, Lancaster University Management School, Economics Department.
    44. Jelena Bradic & Stefan Wager & Yinchu Zhu, 2019. "Sparsity Double Robust Inference of Average Treatment Effects," Papers 1905.00744, arXiv.org.
    45. Daniel Garcia & Juha Tolvanen & Alexander K. Wagner, 2022. "Demand Estimation Using Managerial Responses to Automated Price Recommendations," Management Science, INFORMS, vol. 68(11), pages 7918-7939, November.
    46. Bastien Michel & Samuel Kembou & Sonali Wayal & Joanna Murray, 2023. "Leveraging Mobile Phone Expansion in LMICs to Improve Parental Practices," Working Papers hal-03909663, HAL.
    47. Dominick Bartelme & Ting Lan & Mr. Andrei A Levchenko, 2024. "Specialization, Market Access and Real Income," IMF Working Papers 2024/051, International Monetary Fund.
    48. Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2019. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany’s programmes for long term unemployed," Economics Working Paper Series 1910, University of St. Gallen, School of Economics and Political Science.
    49. Chenchuan (Mark) Li & Ulrich K. Müller, 2021. "Linear regression with many controls of limited explanatory power," Quantitative Economics, Econometric Society, vol. 12(2), pages 405-442, May.
    50. Lechner, Michael & Knaus, Michael C. & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," CEPR Discussion Papers 13402, C.E.P.R. Discussion Papers.
    51. Marcus T. Allen & Justin D. Benefield & Ronald C. Rutherford, 2023. "Co-Listing Strategies: Better Transaction Outcomes?," The Journal of Real Estate Finance and Economics, Springer, vol. 67(3), pages 517-544, October.
    52. Anders Bredahl Kock & Haihan Tang, 2014. "Inference in High-dimensional Dynamic Panel Data Models," CREATES Research Papers 2014-58, Department of Economics and Business Economics, Aarhus University.
    53. Adam McCloskey, 2012. "Bonferroni-Based Size-Correction for Nonstandard Testing Problems," Working Papers 2012-16, Brown University, Department of Economics.
    54. Erjie Ang & Sara Kwasnick & Mohsen Bayati & Erica L. Plambeck & Michael Aratow, 2016. "Accurate Emergency Department Wait Time Prediction," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 141-156, February.
    55. Duso, Tomaso & Michelsen, Claus & Schaefer, Maximilian & Tran, Kevin, 2021. "Airbnb and Rental Markets: Evidence from Berlin," CEPR Discussion Papers 16150, C.E.P.R. Discussion Papers.
    56. Andres, Leander & Fabel, Marc & Rainer, Helmut, 2023. "How much violence does football hooliganism cause?," Journal of Public Economics, Elsevier, vol. 225(C).
    57. Hector Espinoza & Stefan Speckesser, 2019. "A Comparison of Earnings Related to Higher Level Vocational/Technical and Academic Education," National Institute of Economic and Social Research (NIESR) Discussion Papers 502, National Institute of Economic and Social Research.
    58. Robert Adamek & Stephan Smeekes & Ines Wilms, 2024. "Local projection inference in high dimensions," The Econometrics Journal, Royal Economic Society, vol. 27(3), pages 323-342.
    59. Islam, Asad & Lee, Wang-Sheng & Nicholas, Aaron, 2021. "The Effects of Chess Instruction on Academic and Non-cognitive Outcomes: Field Experimental Evidence from a Developing Country," Journal of Development Economics, Elsevier, vol. 150(C).
    60. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    61. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
    62. Jong Hee Park & Byung Koo Kim, 2020. "Why your neighbor matters: Positions in preferential trade agreement networks and export growth in global value chains," Economics and Politics, Wiley Blackwell, vol. 32(3), pages 381-410, November.
    63. Giuseppe Albanese & Emma Galli & Ilde Rizzo & Carla Scaglioni, 2021. "Transparency, civic capital and political accountability: A virtuous relation?," Kyklos, Wiley Blackwell, vol. 74(2), pages 155-169, May.
    64. Yoici Arai & Taisuke Otsu & Myung Hwan Seo, 2022. "Regression discontinuity design with potentially many covariates," STICERD - Econometrics Paper Series 626, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    65. Yoici Arai & Taisuke Otsu & Myung Hwan Seo, 2019. "Causal inference on regression discontinuity designs by high-dimensional methods," STICERD - Econometrics Paper Series 601, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    66. Christian Hansen & Yuan Liao, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," Papers 1611.09420, arXiv.org, revised Dec 2016.
    67. Bilgin, Rumeysa, 2023. "The Selection Of Control Variables In Capital Structure Research With Machine Learning," SocArXiv e26qf, Center for Open Science.
    68. Svetlana Bryzgalova & Jiantao Huang & Christian Julliard, 2023. "Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models," Journal of Finance, American Finance Association, vol. 78(1), pages 487-557, February.
    69. Jayachandran, Seema & Dhar, Diva & Jain, Tarun, 2018. "Reshaping Adolescents' Gender Attitudes: Evidence from a School-Based Experiment in India," CEPR Discussion Papers 13413, C.E.P.R. Discussion Papers.
    70. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    71. Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Papers 2021_19, Business School - Economics, University of Glasgow.
    72. Guo, Jiaqi & Wang, Qiang & Li, Rongrong, 2024. "Can official development assistance promote renewable energy in sub-Saharan Africa countries? A matter of institutional transparency of recipient countries," Energy Policy, Elsevier, vol. 186(C).
    73. Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2024. "Reprint: Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 239(2).
    74. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
    75. Martin, Stephen & Claxton, Karl & Lomas, James & Longo, Francesco, 2023. "The impact of different types of NHS expenditure on health: Marginal cost per QALY estimates for England for 2016/17," Health Policy, Elsevier, vol. 132(C).
    76. Alexandrov, Alexei & Pittman, Russell & Ukhaneva, Olga, 2018. "Pricing of Complements in the U.S. freight railroads: Cournot versus Coase," MPRA Paper 86279, University Library of Munich, Germany.
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    79. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2019. "lassopack: Model selection and prediction with regularized regression in Stata," Papers 1901.05397, arXiv.org.
    80. Victor Chernozhukov & Chen Huang & Weining Wang, 2021. "Uniform Inference on High-dimensional Spatial Panel Networks," Papers 2105.07424, arXiv.org, revised Sep 2023.
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    83. Peter Eibich, 2021. "Care or self-care? The impact of informal care provision on health behaviour," MPIDR Working Papers WP-2021-005, Max Planck Institute for Demographic Research, Rostock, Germany.
    84. Tijan Bah & C. Batista & Flore Gubert & David Mckenzie, 2023. "Can information and alternatives to irregular migration reduce “backway” migration from The Gambia?," Post-Print hal-04318087, HAL.
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  43. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
    See citations under working paper version above.
  44. Alexandre Belloni & Victor Chernozhukov, 2014. "Posterior inference in curved exponential families under increasing dimensions," Econometrics Journal, Royal Economic Society, vol. 17(2), pages 75-100, June.
    See citations under working paper version above.
  45. Xiaohong Chen & Victor Chernozhukov & Sokbae Lee & Whitney K. Newey, 2014. "Local Identification of Nonparametric and Semiparametric Models," Econometrica, Econometric Society, vol. 82(2), pages 785-809, March.
    See citations under working paper version above.
  46. V. Chernozhukov & C. Hansen, 2013. "Quantile Models with Endogeneity," Annual Review of Economics, Annual Reviews, vol. 5(1), pages 57-81, May.
    See citations under working paper version above.
  47. Victor Chernozhukov & Sokbae Lee & Adam M. Rosen, 2013. "Intersection Bounds: Estimation and Inference," Econometrica, Econometric Society, vol. 81(2), pages 667-737, March.
    See citations under working paper version above.
  48. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    See citations under working paper version above.
  49. Victor Chernozhukov & Iván Fernández‐Val & Jinyong Hahn & Whitney Newey, 2013. "Average and Quantile Effects in Nonseparable Panel Models," Econometrica, Econometric Society, vol. 81(2), pages 535-580, March.
    See citations under working paper version above.
  50. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
    See citations under working paper version above.
  51. A. Belloni & V. Chernozhukov & L. Wang, 2011. "Square-root lasso: pivotal recovery of sparse signals via conic programming," Biometrika, Biometrika Trust, vol. 98(4), pages 791-806.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
    3. Liqian Cai & Arnab Bhattacharjee & Roger Calantone & Taps Maiti, 2019. "Variable Selection with Spatially Autoregressive Errors: A Generalized Moments LASSO Estimator," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 146-200, September.
    4. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023. "Granger Causality Testing in High-Dimensional VARs: A Post-Double-Selection Procedure," Journal of Financial Econometrics, Oxford University Press, vol. 21(3), pages 915-958.
    5. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    6. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers 13/16, Institute for Fiscal Studies.
    7. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    8. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    9. Anders Bredahl Kock, 2013. "Oracle inequalities for high-dimensional panel data models," CREATES Research Papers 2013-20, Department of Economics and Business Economics, Aarhus University.
    10. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers 62/13, Institute for Fiscal Studies.
    11. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    12. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Central limit theorems and multiplier bootstrap when p is much larger than n," CeMMAP working papers CWP45/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    15. Laura Freijeiro‐González & Manuel Febrero‐Bande & Wenceslao González‐Manteiga, 2022. "A Critical Review of LASSO and Its Derivatives for Variable Selection Under Dependence Among Covariates," International Statistical Review, International Statistical Institute, vol. 90(1), pages 118-145, April.
    16. Sardy, Sylvain & Diaz-Rodriguez, Jairo & Giacobino, Caroline, 2022. "Thresholding tests based on affine LASSO to achieve non-asymptotic nominal level and high power under sparse and dense alternatives in high dimension," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
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    35. Francesco Decarolis & Cristina Giorgiantonio, 2020. "Corruption red flags in public procurement: new evidence from Italian calls for tenders," Questioni di Economia e Finanza (Occasional Papers) 544, Bank of Italy, Economic Research and International Relations Area.
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    57. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers CWP70/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    87. Jana Janková & Sara Geer, 2017. "Honest confidence regions and optimality in high-dimensional precision matrix estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 143-162, March.
    88. Pun, Chi Seng & Hadimaja, Matthew Zakharia, 2021. "A self-calibrated direct approach to precision matrix estimation and linear discriminant analysis in high dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    89. Quoc Tran-Dinh, 2017. "Adaptive smoothing algorithms for nonsmooth composite convex minimization," Computational Optimization and Applications, Springer, vol. 66(3), pages 425-451, April.
    90. Fan, Jianqing & Feng, Yang & Xia, Lucy, 2020. "A projection-based conditional dependence measure with applications to high-dimensional undirected graphical models," Journal of Econometrics, Elsevier, vol. 218(1), pages 119-139.
    91. Barbara Guardabascio & Filippo Moauro & Luke Mosley, 2024. "Indirect estimation of the monthly transport turnover indicator in Italy," Empirical Economics, Springer, vol. 67(2), pages 531-566, August.
    92. Luke Mosley & Idris Eckley & Alex Gibberd, 2021. "Sparse Temporal Disaggregation," Papers 2108.05783, arXiv.org, revised Oct 2022.
    93. Zanhua Yin, 2020. "Variable selection for sparse logistic regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(7), pages 821-836, October.
    94. Wang, Lie, 2013. "The L1 penalized LAD estimator for high dimensional linear regression," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 135-151.
    95. Alexis Derumigny, 2017. "Improved bounds for Square-Root Lasso and Square-Root Slope," Working Papers 2017-53, Center for Research in Economics and Statistics.
    96. Tianxi Cai & T. Tony Cai & Zijian Guo, 2021. "Optimal statistical inference for individualized treatment effects in high‐dimensional models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 669-719, September.

  52. Victor Chernozhukov & Iván Fernández-Val, 2011. "Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(2), pages 559-589.
    See citations under working paper version above.
  53. Victor Chernozhukov & Iván Fernández-Val & Alfred Galichon, 2010. "Rearranging Edgeworth–Cornish–Fisher expansions," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 42(2), pages 419-435, February.
    See citations under working paper version above.
  54. Victor Chernozhukov & Roberto Rigobon & Thomas M. Stoker, 2010. "Set identification and sensitivity analysis with Tobin regressors," Quantitative Economics, Econometric Society, vol. 1(2), pages 255-277, November.

    Cited by:

    1. Chalak, Karim & Kim, Daniel, 2020. "Measurement error in multiple equations: Tobin’s q and corporate investment, saving, and debt," Journal of Econometrics, Elsevier, vol. 214(2), pages 413-432.
    2. Kazi Musa & Kazi Sohag & Jamaliah Said & Farha Ghapar & Norli Ali, 2023. "Public Debt, Governance, and Growth in Developing Countries: An Application of Quantile via Moments," Mathematics, MDPI, vol. 11(3), pages 1-13, January.
    3. Karim Chalak, 2012. "Identification of Average Random Coefficients under Magnitude and Sign Restrictions on Confounding," Boston College Working Papers in Economics 816, Boston College Department of Economics.
    4. Juan Carlos Escanciano & Lin Zhu, 2013. "Set inferences and sensitivity analysis in semiparametric conditionally identified models," CeMMAP working papers 55/13, Institute for Fiscal Studies.
    5. Chalak, Karim, 2024. "Nonparametric Gini-Frisch bounds," Journal of Econometrics, Elsevier, vol. 238(1).
    6. Travis A. Smith, 2017. "Do School Food Programs Improve Child Dietary Quality?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 99(2), pages 339-356.
    7. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.

  55. Victor Chernozhukov & Iv·n Fern·ndez-Val & Alfred Galichon, 2010. "Quantile and Probability Curves Without Crossing," Econometrica, Econometric Society, vol. 78(3), pages 1093-1125, May.
    See citations under working paper version above.
  56. V. Chernozhukov & I. Fernández-Val & A. Galichon, 2009. "Improving point and interval estimators of monotone functions by rearrangement," Biometrika, Biometrika Trust, vol. 96(3), pages 559-575.
    See citations under working paper version above.
  57. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2009. "Admissible Invariant Similar Tests For Instrumental Variables Regression," Econometric Theory, Cambridge University Press, vol. 25(3), pages 806-818, June.

    Cited by:

    1. Tetsuya Kaji, 2019. "Theory of Weak Identification in Semiparametric Models," Papers 1908.10478, arXiv.org, revised Aug 2020.
    2. Antoine, Bertille & Lavergne, Pascal, 2023. "Identification-robust nonparametric inference in a linear IV model," Journal of Econometrics, Elsevier, vol. 235(1), pages 1-24.
    3. Barnichon, Regis & Mesters, Geert, 2019. "Identifying Modern Macro Equations with Old Shocks," CEPR Discussion Papers 13765, C.E.P.R. Discussion Papers.
    4. Donald W. K. Andrews & Patrik Guggenberger, 2015. "Identification- and Singularity-Robust Inference for Moment Condition," Cowles Foundation Discussion Papers 1978R2, Cowles Foundation for Research in Economics, Yale University, revised Jan 2019.
    5. Marmer, Vadim & Yu, Zhengfei, 2015. "Efficient Inference in the Classical IV Regression Model with Weak Identification: Asymptotic Power Against Arbitrarily Large Deviations from the Null Hypothesis," Microeconomics.ca working papers vadim_marmer-2015-17, Vancouver School of Economics, revised 02 Sep 2015.
    6. Bertille Antoine & Pascal Lavergne, 2020. "Identification-Robust Nonparametric Interference in a Linear IV Model," Discussion Papers dp20-03, Department of Economics, Simon Fraser University.
    7. Kleibergen, Frank, 2021. "Efficient size correct subset inference in homoskedastic linear instrumental variables regression," Journal of Econometrics, Elsevier, vol. 221(1), pages 78-96.
    8. Mills, Benjamin & Moreira, Marcelo J. & Vilela, Lucas P., 2014. "Tests based on t-statistics for IV regression with weak instruments," Journal of Econometrics, Elsevier, vol. 182(2), pages 351-363.
    9. Dong Jin Lee, 2020. "Optimal tests for parameter breaking process in conditional quantile models," The Japanese Economic Review, Springer, vol. 71(3), pages 479-510, July.
    10. Moreira, Humberto Ataíde & Moreira, Marcelo J., 2013. "Contributions to the Theory of Optimal Tests," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 747, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    11. Giuseppe Cavaliere & Zeng-Hua Lu & Anders Rahbek & Yuhong Yang, 2021. "MinP Score Tests with an Inequality Constrained Parameter Space," Papers 2107.06089, arXiv.org.
    12. Aragón, Edilean Kleber da Silva Bejarano & Galvão, Ana Beatriz, 2023. "Shock-based inference on the Phillips curve with the cost channel," Economic Modelling, Elsevier, vol. 126(C).

  58. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2009. "Finite sample inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 152(2), pages 93-103, October.

    Cited by:

    1. Javier Alejo & Antonio F Galvao & Gabriel Montes-Rojas, 2023. "A first-stage representation for instrumental variables quantile regression," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 350-377.
    2. Kaplan, David M. & Sun, Yixiao, 2012. "Smoothed Estimating Equations For Instrumental Variables Quantile Regression," University of California at San Diego, Economics Working Paper Series qt888657tp, Department of Economics, UC San Diego.
    3. Laffers, Lukas, 2013. "Identification in Models with Discrete Variables," Discussion Paper Series in Economics 1/2013, Norwegian School of Economics, Department of Economics.
    4. Fan, Yanqin & Liu, Ruixuan, 2016. "A direct approach to inference in nonparametric and semiparametric quantile models," Journal of Econometrics, Elsevier, vol. 191(1), pages 196-216.
    5. David M. Kaplan, 2013. "IDEAL Inference on Conditional Quantiles via Interpolated Duals of Exact Analytic L-statistics," Working Papers 1316, Department of Economics, University of Missouri.
    6. Chung, Ray S.W. & So, Mike K.P. & Chu, Amanda M.Y. & Chan, Thomas W.C., 2020. "Regularization of Bayesian quasi-likelihoods constructed from complex estimating functions," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    7. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    8. Jun, Sung Jae, 2008. "Weak identification robust tests in an instrumental quantile model," Journal of Econometrics, Elsevier, vol. 144(1), pages 118-138, May.
    9. Luger, Richard, 2012. "Finite-sample bootstrap inference in GARCH models with heavy-tailed innovations," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3198-3211.
    10. David M. Kaplan & Matt Goldman, 2013. "IDEAL Quantile Inference via Interpolated Duals of Exact Analytic L-statistics," Working Papers 1315, Department of Economics, University of Missouri.
    11. David M. Kaplan & Matt Goldman, 2015. "Nonparametric inference on conditional quantile differences and linear combinations, using L-statistics," Working Papers 1503, Department of Economics, University of Missouri.
    12. Joel L. Horowitz, 2017. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers CWP46/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Victor Chernozhukov & Christian Hansen, 2013. "Quantile models with endogeneity," CeMMAP working papers CWP25/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Biying Yu & Junyi Zhang & Akimasa Fujiwara, 2016. "Who rebounds in the private transport sector? A comparative analysis between Beijing and Tokyo," Environment and Planning B, , vol. 43(3), pages 561-579, May.
    15. Olivier Gossner & Karl H. Schlag, 2013. "Finite-sample exact tests for linear regressions with bounded dependent variables," Post-Print halshs-00879792, HAL.
    16. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    17. Zhongjun Qu & Jungmo Yoon, 2011. "Nonparametric Estimation and Inference on Conditional Quantile Processes," Boston University - Department of Economics - Working Papers Series WP2011-059, Boston University - Department of Economics.
    18. Adam Rosen & Takuya Ura, 2020. "Finite Sample Inference for the Maximum Score Estimand," CeMMAP working papers CWP22/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. Elise Coudin & Jean-Marie Dufour, 2010. "Finite and Large Sample Distribution-Free Inference in Median Regressions with Instrumental Variables," Working Papers 2010-56, Center for Research in Economics and Statistics.
    20. de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019. "Smoothed GMM for quantile models," Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
    21. Su Liangjun & Tadao Hoshino, 2015. "Sieve Instrumental Variable Quantile Regression Estimation of Functional Coefficient Models," Working Papers 01-2015, Singapore Management University, School of Economics.
    22. David M. Kaplan, 2020. "sivqr: Smoothed IV quantile regression," Working Papers 2009, Department of Economics, University of Missouri.
    23. David M. Kaplan, 2023. "Smoothed instrumental variables quantile regression," Papers 2310.09013, arXiv.org.
    24. Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," Journal of Econometrics, Elsevier, vol. 210(2), pages 219-235.
    25. Kaspar W thrich, 2014. "A Comparison of two Quantile Models with Endogeneity," Diskussionsschriften dp1408, Universitaet Bern, Departement Volkswirtschaft.
    26. Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.
    27. Hiroaki Kaido & Kaspar Wüthrich, 2018. "Decentralization estimators for instrumental variable quantile regression models," CeMMAP working papers CWP72/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    28. Zhongjun Qu & Jungmo Yoon, 2015. "Uniform Inference on Quantile Effects under Sharp Regression Discontinuity Designs," Boston University - Department of Economics - Working Papers Series wp2015-009, Boston University - Department of Economics.
    29. Purevdorj Tuvaandorj, 2021. "Robust Permutation Tests in Linear Instrumental Variables Regression," Papers 2111.13774, arXiv.org, revised Jul 2024.
    30. Joel L. Horowitz, 2017. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers 46/17, Institute for Fiscal Studies.
    31. Joel L. Horowitz, 2018. "Non-Asymptotic Inference in Instrumental Variables Estimation," Papers 1809.03600, arXiv.org.
    32. Horowitz, Joel L., 2021. "Bounding the difference between true and nominal rejection probabilities in tests of hypotheses about instrumental variables models," Journal of Econometrics, Elsevier, vol. 222(2), pages 1057-1082.
    33. Kaspar W thrich, 2015. "Semiparametric estimation of quantile treatment effects with endogeneity," Diskussionsschriften dp1509, Universitaet Bern, Departement Volkswirtschaft.
    34. Marina Dias & Demian Pouzo, 2021. "Inference for multi-valued heterogeneous treatment effects when the number of treated units is small," Papers 2105.10965, arXiv.org.
    35. Joel L. Horowitz, 2018. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers CWP52/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    36. Isaiah Andrews & Anna Mikusheva, 2022. "Optimal Decision Rules for Weak GMM," Econometrica, Econometric Society, vol. 90(2), pages 715-748, March.

  59. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.

    Cited by:

    1. Klaus Friesenbichler & Agnes Kügler & Andreas Reinstaller, 2023. "The Impact of Import Competition from China on Firm-level Productivity Growth in the EU," Research Papers 0323, Büro des Produktivitätsrates.
    2. João Santos Silva, 2019. "Quantile regression: Basics and recent advances," London Stata Conference 2019 27, Stata Users Group.
    3. Raghuram G. Rajan & Rodney Ramcharan, 2009. "Land and Credit: A Study of the Political Economy of Banking in the United States in the Early 20th Century," NBER Working Papers 15083, National Bureau of Economic Research, Inc.
    4. Galvao, Antonio F. & Montes-Rojas, Gabriel, 2015. "On the equivalence of instrumental variables estimators for linear models," Economics Letters, Elsevier, vol. 134(C), pages 13-15.
    5. Xu, Xiu & Wang, Weining & Shin, Yongcheol, 2020. "Dynamic Spatial Network Quantile Autoregression," IRTG 1792 Discussion Papers 2020-024, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    6. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
    7. Chen, Songnian, 2018. "Sequential estimation of censored quantile regression models," Journal of Econometrics, Elsevier, vol. 207(1), pages 30-52.
    8. Michael L. Polemis, 2020. "A note on the estimation of competition-productivity nexus: a panel quantile approach," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(4), pages 663-676, December.
    9. Galina Besstremyannaya, 2014. "The adverse effects of value-based purchasing in health care: dynamic quantile regression with endogeneity," Discussion Papers 14-006, Stanford Institute for Economic Policy Research.
    10. Tae-Hwan Kim & Christophe Muller, 2020. "Inconsistency transmission and variance reduction in two-stage quantile regression," Post-Print hal-02084505, HAL.
    11. Javier Alejo & Antonio F Galvao & Gabriel Montes-Rojas, 2023. "A first-stage representation for instrumental variables quantile regression," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 350-377.
    12. Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2017. "Smoothed GMM for quantile models," Papers 1707.03436, arXiv.org, revised Feb 2018.
    13. Ozan Bakis & Nurhan Davutyan & Haluk Levent & Sezgin Polat, 2010. "External Returns to Higher Education in Turkey," Working Papers 517, Economic Research Forum, revised 04 Jan 2010.
    14. Erik Figueiredo & Luiz Renato Lima & Gianluca Orefice, 2016. "Migration and Regional Trade Agreements: A (New) Gravity Estimation," Review of International Economics, Wiley Blackwell, vol. 24(1), pages 99-125, February.
    15. Cizek, P., 2009. "Generalized Methods of Trimmed Moments," Other publications TiSEM 46607f30-95c0-430a-8ef9-2, Tilburg University, School of Economics and Management.
    16. Kaplan, David M. & Sun, Yixiao, 2012. "Smoothed Estimating Equations For Instrumental Variables Quantile Regression," University of California at San Diego, Economics Working Paper Series qt888657tp, Department of Economics, UC San Diego.
    17. Dambala Gelo & Steven F. Koch & Edwin Muchapondwah, 2013. "Do the Poor Benefit from Devolution Policies? Evidences from Quantile Treatment Effect Evaluation of Joint Forest Management," Working Papers 201388, University of Pretoria, Department of Economics.
    18. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2009. "Finite sample inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 152(2), pages 93-103, October.
    19. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    20. Amanda Kowalski, 2016. "Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 107-117, January.
    21. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    22. Xin Liu, 2024. "A quantile-based nonadditive fixed effects model," Papers 2405.03826, arXiv.org.
    23. Barceló, Cristina & Villanueva, Ernesto, 2016. "The response of household wealth to the risk of job loss: Evidence from differences in severance payments," Labour Economics, Elsevier, vol. 39(C), pages 35-54.
    24. Alessia Matano & Paolo Naticchioni, 2017. "The Extent of Rent Sharing along the Wage Distribution," IREA Working Papers 201704, University of Barcelona, Research Institute of Applied Economics, revised Feb 2017.
    25. Aluko, Olufemi Adewale & Opoku, Eric Evans Osei, 2022. "The financial development impact of financial globalization revisited: A focus on OECD countries," International Economics, Elsevier, vol. 169(C), pages 13-29.
    26. Tae-Hwan Kim & Christophe Muller, 2017. "A Robust Test of Exogeneity Based on Quantile Regressions," Working Papers halshs-01508067, HAL.
    27. Söderlund, Bengt & Gustavsson Tingvall, Patrik, 2014. "Capital Freedom, Financial Development and Provincial Economic Growth in China," Ratio Working Papers 234, The Ratio Institute.
    28. M. Fort, 2012. "Unconditional and Conditional Quantile Treatment Effect: Identification Strategies and Interpretations," Working Papers wp857, Dipartimento Scienze Economiche, Universita' di Bologna.
    29. Nguyen, Quang Khai, 2022. "Audit committee structure, institutional quality, and bank stability: evidence from ASEAN countries," Finance Research Letters, Elsevier, vol. 46(PA).
    30. Ardizzi, Guerino, 2013. "Card versus cash: empirical evidence of the impact of payment card interchange fees on end users’ choice of payment methods," MPRA Paper 48088, University Library of Munich, Germany, revised 25 May 2013.
    31. Valizadeh, Pourya & Smith, Travis A., 2018. "Distributional Impacts of WIC on Dietary Quality of Children: Evidence from a Regression Discontinuity Design," 2018 Annual Meeting, August 5-7, Washington, D.C. 273904, Agricultural and Applied Economics Association.
    32. Chang, Chun-Ping & Wen, Jun & Dong, Minyi & Hao, Yu, 2018. "Does government ideology affect environmental pollutions? New evidence from instrumental variable quantile regression estimations," Energy Policy, Elsevier, vol. 113(C), pages 386-400.
    33. David Powell, 2013. "A New Framework for Estimation of Quantile Treatment Effects Nonseparable Disturbance in the Presence of Covariates," Working Papers WR-824-1, RAND Corporation.
    34. Jun, Sung Jae, 2008. "Weak identification robust tests in an instrumental quantile model," Journal of Econometrics, Elsevier, vol. 144(1), pages 118-138, May.
    35. Hammoudeh, Shawkat & Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Adeabah, David, 2023. "Global value chains in sub-Saharan Africa: The role of business regulations, policies and institutions," Emerging Markets Review, Elsevier, vol. 57(C).
    36. Jau-er Chen & Chien-Hsun Huang & Jia-Jyun Tien, 2021. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
    37. Alessie, R.J.M. & Angelini, V. & van Santen, P.C., 2012. "Pension wealth and household savings in Europe," Research Report 12012-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    38. Tommaso Gabrieli & Antonio F. Galvao, Jr. & Antonio F. Galvao, Jr., 2010. "Who Benefits from Reducing the Cost of Formality? Quantile Regression Discontinuity Analysis," Real Estate & Planning Working Papers rep-wp2010-11, Henley Business School, University of Reading.
    39. Klomp, Jeroen, 2013. "Government interventions and default risk: Does one size fit all?," Journal of Financial Stability, Elsevier, vol. 9(4), pages 641-653.
    40. Marcelo Fernandes & Emmanuel Guerre & Eduardo Horta, 2021. "Smoothing Quantile Regressions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 338-357, January.
    41. Kaushal, Kaushalendra Kumar, 2014. "Pathway from nutrition intake to wage among elementary workers in India," MPRA Paper 56652, University Library of Munich, Germany.
    42. Chunbei Wang & Le Wang, 2011. "Language Skills and the Earnings Distribution Among Child Immigrants," Industrial Relations: A Journal of Economy and Society, Wiley Blackwell, vol. 50(2), pages 297-322, April.
    43. Gabriel Montes-Rojas, 2011. "Quantile Regression with Classical Additive Measurement Errors," Economics Bulletin, AccessEcon, vol. 31(4), pages 2863-2868.
    44. Kassouri, Yacouba, 2022. "Fiscal decentralization and public budgets for energy RD&D: A race to the bottom?," Energy Policy, Elsevier, vol. 161(C).
    45. Erik Figueiredo & Luiz Renato Lima & Gianluca Orefice, 2016. "Third Country Effect of Migration: the Trade-Migration Nexus Revisited," Working Papers 2016-22, CEPII research center.
    46. Jinping Zhang & Qiuru Lu & Li Guan & Xiaoying Wang, 2021. "Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China," Energies, MDPI, vol. 14(2), pages 1-17, January.
    47. David Powell & Joachim Wagner, 2011. "The Exporter Productivity Premium along the Productivity Distribution Evidence from Unconditional Quantile Regression with Firm Fixed Effects," Working Papers WR-837, RAND Corporation.
    48. Fabio Pieri, 2016. "Vertical organization of production and firm growth," DEM Working Papers 2016/01, Department of Economics and Management.
    49. Dima, Bogdan & Dincă, Marius Sorin & Spulbăr, Cristi, 2014. "Financial nexus: Efficiency and soundness in banking and capital markets," Journal of International Money and Finance, Elsevier, vol. 47(C), pages 100-124.
    50. Karen Clay & Jeff Lingwall & Melvin Stephens, Jr., 2016. "Laws, Educational Outcomes, and Returns to Schooling: Evidence from the Full Count 1940 Census," NBER Working Papers 22855, National Bureau of Economic Research, Inc.
    51. Mehmet Balcilar & Ojonugwa Usman & Mark Wohar & David Roubaud & Hasan Gungor, 2024. "Global liquidity effect of quantitative easing on emerging markets," Empirical Economics, Springer, vol. 67(6), pages 2449-2461, December.
    52. Dolado, Juan J & Chen, Liang & Gonzalo, Jesus, 2018. "Quantile Factor Models," CEPR Discussion Papers 12716, C.E.P.R. Discussion Papers.
    53. Andrés Rodríguez-Pose & Alexandra Sotiriou, 2021. "Trading with richer and poorer countries: trade integration and regional inequality in Greece," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 67(3), pages 697-725, December.
    54. David Powell, 2010. "Unconditional Quantile Treatment Effects in the Presence of Covariates," Working Papers WR-816, RAND Corporation.
    55. Chen Jau-er, 2015. "Factor instrumental variable quantile regression," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(1), pages 71-92, February.
    56. Haiyang Lu & Ivan T. Kandilov, 2021. "Does Mobile Internet Use Affect the Subjective Well-being of Older Chinese Adults? An Instrumental Variable Quantile Analysis," Journal of Happiness Studies, Springer, vol. 22(7), pages 3137-3156, October.
    57. Daniel L. Millimet & Ian K. McDonough, 2017. "Dynamic Panel Data Models With Irregular Spacing: With an Application to Early Childhood Development," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 725-743, June.
    58. Paul Contoyannis & Jinhu Li, 2014. "The Dynamics of Depression from Adolescence to Early Adulthood," Department of Economics Working Papers 2014-09, McMaster University.
    59. Xichen Wang & Cheng Yan, 2022. "Does the Relative Importance of the Push and Pull Factors of Foreign Capital Flows Vary Across Quantiles?," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 70(2), pages 252-299, June.
    60. Bowei Li & Qiyan Zeng, 2022. "The Effect of Land Right Stability on the Application of Fertilizer Reduction Technologies—Evidence from Large-Scale Farmers in China," Sustainability, MDPI, vol. 14(13), pages 1-13, July.
    61. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    62. Alessie, Rob & Angelini, Viola & van Santen, Peter, 2013. "Pension Wealth and Household Savings in Europe: Evidence from SHARELIFE," Working Paper Series 265, Sveriges Riksbank (Central Bank of Sweden).
    63. Li Tao & Yuanjie Zhang & Maozai Tian, 2019. "Quantile Regression for Dynamic Panel Data Using Hausman–Taylor Instrumental Variables," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1033-1069, March.
    64. Xiaowen Dai & Libin Jin, 2021. "Minimum distance quantile regression for spatial autoregressive panel data models with fixed effects," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-13, December.
    65. Yunpeng Wang & Chun-Ping Chang, 2023. "The effect of policy stability on clean energy investment," Economic Change and Restructuring, Springer, vol. 56(1), pages 327-344, February.
    66. Guillaume Chapelle & J.B. Eyméoud & C. Wolf, 2023. "Land-use regulation and housing supply elasticity: evidence from France," THEMA Working Papers 2023-08, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    67. Egger, Peter H. & Seliger, Florian & Woerter, Martin, 2016. "On the distribution of patent citations and its fundamentals," Economics Letters, Elsevier, vol. 147(C), pages 72-77.
    68. Michel Cândido de Souza & Lízia de Figueiredo & Mauro Sayar Ferreira, 2021. "The asymmetric evolution of economic institutions: evidence from dynamic panel quantile regression with iv and fixed effects," Textos para Discussão Cedeplar-UFMG 631, Cedeplar, Universidade Federal de Minas Gerais.
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    90. Jim Been & Susann Rohwedder & Michael Hurd, 2021. "Households’ joint consumption spending and home production responses to retirement in the US," Review of Economics of the Household, Springer, vol. 19(4), pages 959-985, December.
    91. Fatemi, Ali & Glaum, Martin & Kaiser, Stefanie, 2018. "ESG performance and firm value: The moderating role of disclosure," Global Finance Journal, Elsevier, vol. 38(C), pages 45-64.
    92. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2007. "Enhanced routines for instrumental variables/GMM estimation and testing," Boston College Working Papers in Economics 667, Boston College Department of Economics, revised 05 Sep 2007.
    93. Ines Buono & Sara Formai, 2019. "Bank credit, liquidity and firm-level investment: are recessions different?," Temi di discussione (Economic working papers) 1239, Bank of Italy, Economic Research and International Relations Area.
    94. Yoko Ibuka & Haruo Kakehi & Ryuki Kobayashi & Ryo Nakajima, 2024. "The effect of staying at home on suicide during the COVID-19 pandemic," Keio-IES Discussion Paper Series 2024-004, Institute for Economics Studies, Keio University.
    95. Katrina Kosec & Jie Song, 2021. "The effects of income fluctuations on undernutrition and overnutrition across the lifecycle," Health Economics, John Wiley & Sons, Ltd., vol. 30(10), pages 2487-2509, September.
    96. Kristine M. Brown & Ron A. Laschever, 2012. "When They're Sixty-Four: Peer Effects and the Timing of Retirement," American Economic Journal: Applied Economics, American Economic Association, vol. 4(3), pages 90-115, July.
    97. Theodore F. Figinski & Alicia Lloro & Avinash Moorthy, 2022. "Revisiting the Effect of Education on Later Life Health," Finance and Economics Discussion Series 2022-007, Board of Governors of the Federal Reserve System (U.S.).
    98. Keith Finlay & Leandro M. Magnusson, 2014. "Bootstrap Methods for Inference with Cluster-Sample IV Models," Economics Discussion / Working Papers 14-12, The University of Western Australia, Department of Economics.
    99. Hufe, Paul, 2024. "The Parental Wage Gap and the Development of Socio-Emotional Skills in Children," IZA Discussion Papers 16977, Institute of Labor Economics (IZA).
    100. Carlo Medici, 2024. "Closing Ranks: Organized Labor and Immigration," CESifo Working Paper Series 11437, CESifo.
    101. Xu, Guo, 2019. "The colonial origins of fiscal capacity: Evidence from patronage governors," Journal of Comparative Economics, Elsevier, vol. 47(2), pages 263-276.

  61. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2007. "Inference approaches for instrumental variable quantile regression," Economics Letters, Elsevier, vol. 95(2), pages 272-277, May.

    Cited by:

    1. Andini, Corrado, 2017. "Tertiary Education for All and Wage Inequality: Policy Insights from Quantile Regression," IZA Policy Papers 132, Institute of Labor Economics (IZA).
    2. Javier Alejo & Antonio F Galvao & Gabriel Montes-Rojas, 2023. "A first-stage representation for instrumental variables quantile regression," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 350-377.
    3. Le-Yu Chen & Sokbae (Simon) Lee, 2017. "Exact computation of GMM estimators for instrumental variable quantile regression models," CeMMAP working papers CWP52/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Bryan S. Graham & Jinyong Hahn & Alexandre Poirier & James L. Powell, 2016. "A quantile correlated random coefficients panel data model," CeMMAP working papers 34/16, Institute for Fiscal Studies.
    5. WenShwo Fang & Stephen M. Miller & Chih-Chuan Yeh, 2009. "Does a Threshold Inflation Rate Exist? Quantile Inferences for Inflation and Its Variability," Working Papers 0921, University of Nevada, Las Vegas , Department of Economics, revised Dec 2009.
    6. Domenico Depalo & Raffaella Giordano, 2011. "The public-private pay gap: a robust quantile approach," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 70(1), pages 25-64, January.
    7. Jean-Jacques Forneron, 2019. "Detecting Identification Failure in Moment Condition Models," Papers 1907.13093, arXiv.org, revised Oct 2023.
    8. Santiago Budría, 2011. "Are Educational Mismatches Responsible for the ‘Inequality Increasing Effect’ of Education?," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 102(3), pages 409-437, July.
    9. Marilena Furno, 2020. "Returns to Education and Gender Wage Gap Across Quantiles in Italy," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 12(2), pages 145-169, June.
    10. Le-Yu Chen & Yu-Min Yen, 2021. "Estimations of the Local Conditional Tail Average Treatment Effect," Papers 2109.08793, arXiv.org, revised May 2024.
    11. Arabsheibani, Reza & Staneva, Anita, 2012. "Returns to Education in Russia: Where There Is Risky Sexual Behaviour There Is Also an Instrument," IZA Discussion Papers 6726, Institute of Labor Economics (IZA).
    12. Wehby George L. & McCarthy Ann Marie & Castilla Eduardo & Murray Jeffrey C., 2011. "The Impact of Household Investments on Early Child Neurodevelopment and on Racial and Socioeconomic Developmental Gaps: Evidence from South America," Forum for Health Economics & Policy, De Gruyter, vol. 14(1), pages 1-60, December.
    13. Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," Journal of Econometrics, Elsevier, vol. 210(2), pages 219-235.
    14. Wehby, George L. & Murray, Jeffrey C. & Wilcox, Allen & Lie, Rolv T., 2012. "Smoking and body weight: Evidence using genetic instruments," Economics & Human Biology, Elsevier, vol. 10(2), pages 113-126.
    15. Kaspar W thrich, 2014. "A Comparison of two Quantile Models with Endogeneity," Diskussionsschriften dp1408, Universitaet Bern, Departement Volkswirtschaft.
    16. Wang, Le, 2011. "How Does Education Affect the Earnings Distribution in Urban China?," IZA Discussion Papers 6173, Institute of Labor Economics (IZA).
    17. George L. Wehby & Jeffrey C. Murray & Eduardo E. Castilla & Jorge S. Lopez‐Camelo & Robert L. Ohsfeldt, 2009. "Quantile effects of prenatal care utilization on birth weight in Argentina," Health Economics, John Wiley & Sons, Ltd., vol. 18(11), pages 1307-1321, November.
    18. Morra, Wayne & Hearn, Gail & Buck, Andrew J., 2009. "The market for bushmeat: Colobus Satanas on Bioko Island," Ecological Economics, Elsevier, vol. 68(10), pages 2619-2626, August.
    19. Andini, Corrado & Andini, Monica, 2015. "A Note on Unemployment Persistence and Quantile Parameter Heterogeneity," IZA Discussion Papers 8819, Institute of Labor Economics (IZA).
    20. Luis Gamboa & Mauricio Rodríguez & Andrés García, 2013. "Differences in motivations and academic achievement," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 78, pages 9-44.
    21. Oliver Himmler, 2009. "The Effects of School Competition on Academic Achievement and Grading Standards," CESifo Working Paper Series 2676, CESifo.
    22. Kaspar W thrich, 2015. "Semiparametric estimation of quantile treatment effects with endogeneity," Diskussionsschriften dp1509, Universitaet Bern, Departement Volkswirtschaft.

  62. Chernozhukov, Victor & Imbens, Guido W. & Newey, Whitney K., 2007. "Instrumental variable estimation of nonseparable models," Journal of Econometrics, Elsevier, vol. 139(1), pages 4-14, July.

    Cited by:

    1. Depalo, D.;, 2019. "Explaining the causal effect of adherence to medication on cholesterol through the marginal patient," Health, Econometrics and Data Group (HEDG) Working Papers 19/13, HEDG, c/o Department of Economics, University of York.
    2. Stefan Hoderlein & Yuya Sasaki, 2013. "Outcome Conditioned Treatment Effects," Boston College Working Papers in Economics 840, Boston College Department of Economics.
    3. Yingyao Hu & Susanne M. Schennach, 2008. "Instrumental Variable Treatment of Nonclassical Measurement Error Models," Econometrica, Econometric Society, vol. 76(1), pages 195-216, January.
    4. Horowitz, Joel L. & Lee, Sokbae, 2009. "Testing a parametric quantile-regression model with an endogenous explanatory variable against a nonparametric alternative," Journal of Econometrics, Elsevier, vol. 152(2), pages 141-152, October.
    5. Tae-Hwan Kim & Christophe Muller, 2020. "Inconsistency transmission and variance reduction in two-stage quantile regression," Post-Print hal-02084505, HAL.
    6. Xiaohong Chen & Demian Pouzo, 2009. "Efficient estimation of semiparametric conditional moment models with possibly nonsmooth residuals," CeMMAP working papers CWP20/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Joel L. Horowitz & Sokbae (Simon) Lee, 2010. "Uniform confidence bands for functions estimated nonparametrically with instrumental variables," CeMMAP working papers CWP19/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Andrew Chesher, 2007. "Endogeneity and discrete outcomes," CeMMAP working papers CWP05/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Xiaohong Chen & Andres Santos, 2015. "Overidentification in Regular Models," Cowles Foundation Discussion Papers 1999R, Cowles Foundation for Research in Economics, Yale University, revised Jun 2018.
    10. Sasaki, Yuya, 2015. "What Do Quantile Regressions Identify For General Structural Functions?," Econometric Theory, Cambridge University Press, vol. 31(5), pages 1102-1116, October.
    11. Xiaohong Chen & Markus Reiss, 2007. "On Rate Optimality for Ill-posed Inverse Problems in Econometrics," Cowles Foundation Discussion Papers 1626, Cowles Foundation for Research in Economics, Yale University.
    12. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2009. "Finite sample inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 152(2), pages 93-103, October.
    13. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    14. Xiaohong Chen & Demian Pouzo, 2012. "Estimation of Nonparametric Conditional Moment Models With Possibly Nonsmooth Generalized Residuals," Econometrica, Econometric Society, vol. 80(1), pages 277-321, January.
    15. Fusejima, Koki, 2024. "Identification of multi-valued treatment effects with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 238(1).
    16. Xiaohong Chen & Victor Chernozhukov & Sokbae (Simon) Lee & Whitney K. Newey, 2011. "Local identification of nonparametric and semiparametric models," CeMMAP working papers 17/11, Institute for Fiscal Studies.
    17. Xiaohong Chen & Demian Pouzo, 2014. "Sieve Wald and QLR Inferences on Semi/nonparametric Conditional Moment Models," Papers 1411.1144, arXiv.org, revised Mar 2015.
    18. Andrii Babii & Jean-Pierre Florens, 2017. "Are Unobservables Separable?," Papers 1705.01654, arXiv.org, revised Mar 2021.
    19. Gagliardini, Patrick & Scaillet, Olivier, 2012. "Tikhonov regularization for nonparametric instrumental variable estimators," Journal of Econometrics, Elsevier, vol. 167(1), pages 61-75.
    20. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    21. Liu, Chu-An & Tao, Jing, 2016. "Model selection and model averaging in nonparametric instrumental variables models," MPRA Paper 69492, University Library of Munich, Germany.
    22. Komunjer, Ivana, 2007. "Global Identification In Nonlinear Semiparametric Models," University of California at San Diego, Economics Working Paper Series qt8dk0n386, Department of Economics, UC San Diego.
    23. Carneiro, Pedro & Lee, Sokbae, 2009. "Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality," Journal of Econometrics, Elsevier, vol. 149(2), pages 191-208, April.
    24. Xiaohong Chen & Demian Pouzo, 2014. "Sieve Wald and QLR Inferences on Semi/nonparametric Conditional Moment Models," CeMMAP working papers 38/14, Institute for Fiscal Studies.
    25. Nir Billfeld & Moshe Kim, 2024. "Context-dependent Causality (the Non-Nonotonic Case)," Papers 2404.05021, arXiv.org.
    26. Hidehiko Ichimura & Whitney K. Newey, 2017. "The influence function of semiparametric estimators," CeMMAP working papers 06/17, Institute for Fiscal Studies.
    27. Joseph G. Altonji & Hidehiko Ichimura & Taisuke Otsu, 2008. "Estimating Derivatives in Nonseparable Models with Limited Dependent Variables," Cowles Foundation Discussion Papers 1668R, Cowles Foundation for Research in Economics, Yale University, revised May 2011.
    28. Victor Chernozhukov & Christian Hansen, 2013. "Quantile models with endogeneity," CeMMAP working papers CWP25/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    29. Senay Sokullu, 2012. "Nonparametric Analysis of Two-Sided Markets," Bristol Economics Discussion Papers 12/628, School of Economics, University of Bristol, UK.
    30. Jingwen Zhang & Yifang Chen & Amandeep Singh, 2022. "Causal Bandits: Online Decision-Making in Endogenous Settings," Papers 2211.08649, arXiv.org, revised Feb 2023.
    31. Cazals, Catherine & Feve, Frederique & Florens, Jean-Pierre & Simar, Leopold, 2016. "Non Parametric Instrumental Variables Estimation for Efficiency Frontier," LIDAM Reprints ISBA 2016014, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    32. Christophe Muller, 2019. "Linear Quantile Regression and Endogeneity Correction," Working Papers halshs-02272874, HAL.
    33. Hu, Yingyao & Schennach, Susanne M. & Shiu, Ji-Liang, 2017. "Injectivity of a class of integral operators with compactly supported kernels," Journal of Econometrics, Elsevier, vol. 200(1), pages 48-58.
    34. Dunker, Fabian & Florens, Jean-Pierre & Hohage, Thorsten & Johannes, Jan & Mammen, Enno, 2014. "Iterative estimation of solutions to noisy nonlinear operator equations in nonparametric instrumental regression," LIDAM Reprints ISBA 2014007, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    35. Blundell, Richard & Kristensen, Dennis & Matzkin, Rosa, 2014. "Bounding quantile demand functions using revealed preference inequalities," Journal of Econometrics, Elsevier, vol. 179(2), pages 112-127.
    36. Poirier, Alexandre, 2017. "Efficient estimation in models with independence restrictions," Journal of Econometrics, Elsevier, vol. 196(1), pages 1-22.
    37. Susanne M. Schennach & Halbert White & Karim Chalak, 2007. "Estimating average marginal effects in nonseparable structural systems," CeMMAP working papers CWP31/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    38. Yingyao Hu & Ji-Liang Shiu, 2011. "Nonparametric identification using instrumental variables: sufficient conditions for completeness," CeMMAP working papers CWP25/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    39. Chunrong Ai & Xiaohong Chen, 2009. "Semiparametric efficiency bound for models of sequential moment restrictions containing unknown functions," CeMMAP working papers CWP28/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    40. Frédérique Fève & Jean-Pierre Florens, 2010. "The practice of non-parametric estimation by solving inverse problems: the example of transformation models," Econometrics Journal, Royal Economic Society, vol. 13(3), pages 1-27, October.
    41. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    42. Kasy, Maximilian, "undated". "Instrumental variables with unrestricted heterogeneity and continuous treatment - DON'T CITE! SEE ERRATUM BELOW," Working Paper 33257, Harvard University OpenScholar.
    43. Xiaohong Chen & Demian Pouzo, 2008. "Estimation of nonparametric conditional moment models with possibly nonsmooth moments," CeMMAP working papers CWP12/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    44. Chen, Xiaohong & Pouzo, Demian & Powell, James L., 2019. "Penalized sieve GEL for weighted average derivatives of nonparametric quantile IV regressions," Journal of Econometrics, Elsevier, vol. 213(1), pages 30-53.
    45. Giovanni Forchini, 2012. "Structural Equations and Invariance," School of Economics Discussion Papers 0312, School of Economics, University of Surrey.
    46. Jia-Young Michael Fu & Joel L. Horowitz & Matthias Parey, 2015. "Testing exogeneity in nonparametric instrumental variables identified by conditional quantile restrictions," CeMMAP working papers CWP68/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    47. Xiaohong Chen & Yingyao Hu, 2006. "Identification and Inference of Nonlinear Models Using Two Samples with Arbitrary Measurement Errors," Cowles Foundation Discussion Papers 1590, Cowles Foundation for Research in Economics, Yale University.
    48. Andrew Chesher & Adam Rosen, 2014. "Generalized instrumental variable models," CeMMAP working papers 04/14, Institute for Fiscal Studies.
    49. Joel L. Horowitz, 2013. "Adaptive nonparametric instrumental variables estimation: empirical choice of the regularization parameter," CeMMAP working papers CWP30/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    50. Tae-Hwan Kim, & Christophe Muller, 2012. "Bias Transmission and Variance Reduction in Two-Stage Quantile Regression," AMSE Working Papers 1221, Aix-Marseille School of Economics, France.
    51. Christoph Breunig, 2019. "Specification Testing in Nonparametric Instrumental Quantile Regression," Papers 1909.10129, arXiv.org.
    52. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2017. "Non-separable Models with High-dimensional Data," Economics and Statistics Working Papers 15-2017, Singapore Management University, School of Economics.
    53. Feng, Junlong, 2024. "Matching points: Supplementing instruments with covariates in triangular models," Journal of Econometrics, Elsevier, vol. 238(1).
    54. Rothe, Christoph, 2009. "Identification of Unconditional Partial Effects in Non Separable Models," TSE Working Papers 09-054, Toulouse School of Economics (TSE).
    55. Halbert White & Karim Chalak, 2013. "Identification and Identification Failure for Treatment Effects Using Structural Systems," Econometric Reviews, Taylor & Francis Journals, vol. 32(3), pages 273-317, November.
    56. Brancaccio, Giulia & Kalouptsidi, Myrto & Papageorgiou, Theodore, 2020. "A guide to estimating matching functions in spatial models," International Journal of Industrial Organization, Elsevier, vol. 70(C).
    57. Joel L. Horowitz, 2013. "Adaptive nonparametric instrumental variables estimation: empirical choice of the regularization parameter," CeMMAP working papers 30/13, Institute for Fiscal Studies.
    58. Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022. "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers 2211.07903, arXiv.org.
    59. Chen, Xiaohong, 2007. "Large Sample Sieve Estimation of Semi-Nonparametric Models," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 76, Elsevier.
    60. Krief, Jerome M., 2017. "Direct instrumental nonparametric estimation of inverse regression functions," Journal of Econometrics, Elsevier, vol. 201(1), pages 95-107.
    61. Jean-Jacques Forneron, 2019. "A Sieve-SMM Estimator for Dynamic Models," Papers 1902.01456, arXiv.org, revised Jan 2023.
    62. Minji Bang & Wayne Gao & Andrew Postlewaite & Holger Sieg, 2021. "Using Monotonicity Restrictions to Identify Models with Partially Latent Covariates," NBER Working Papers 28436, National Bureau of Economic Research, Inc.
    63. Fabian Dunker, 2015. "Adaptive estimation for some nonparametric instrumental variable models," Papers 1511.03977, arXiv.org, revised Aug 2021.
    64. Lee, Sungwon, 2023. "Efficient estimation of a triangular system of equations for quantile regression," Economics Letters, Elsevier, vol. 226(C).
    65. Nail Kashaev, 2022. "Identification and Estimation of Multinomial Choice Models with Latent Special Covariates," University of Western Ontario, Departmental Research Report Series 20224, University of Western Ontario, Department of Economics.
    66. Tae-Hwan Kim & Christophe Muller, 2015. "A Particular Form of Non-Constant Effect in Two-Stage Quantile Regression," Working papers 2015rwp-82, Yonsei University, Yonsei Economics Research Institute.
    67. Joachim Freyberger & Joel L. Horowitz, 2013. "Identification and shape restrictions in nonparametric instrumental variables estimation," CeMMAP working papers 31/13, Institute for Fiscal Studies.
    68. John C. Ham & Kevin T. Reilly, 2013. "Implicit Contracts, Life Cycle Labor Supply, And Intertemporal Substitution," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 54(4), pages 1133-1158, November.
    69. Xiaohong Chen & Demian Pouzo, 2013. "Sieve Quasi Likelihood Ratio Inference on Semi/nonparametric Conditional Moment Models," Cowles Foundation Discussion Papers 1897, Cowles Foundation for Research in Economics, Yale University.
    70. Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," Journal of Econometrics, Elsevier, vol. 210(2), pages 219-235.
    71. Schennach, Susanne & White, Halbert & Chalak, Karim, 2012. "Local indirect least squares and average marginal effects in nonseparable structural systems," Journal of Econometrics, Elsevier, vol. 166(2), pages 282-302.
    72. Andrews, Donald W.K., 2017. "Examples of L2-complete and boundedly-complete distributions," Journal of Econometrics, Elsevier, vol. 199(2), pages 213-220.
    73. Chiappori, Pierre-Andre & Komunjer, Ivana, 2008. "Correct Specification and Identification of Nonparametric Transformation Models," University of California at San Diego, Economics Working Paper Series qt4v12m2rg, Department of Economics, UC San Diego.
    74. Andrew Chesher, 2008. "Instrumental variable models for discrete outcomes," CeMMAP working papers CWP30/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    75. An, Yonghong & Hu, Yingyao & Liu, Pengfei, 2018. "Estimating heterogeneous contributing strategies in threshold public goods provision: A structural analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 152(C), pages 124-146.
    76. Kaspar W thrich, 2014. "A Comparison of two Quantile Models with Endogeneity," Diskussionsschriften dp1408, Universitaet Bern, Departement Volkswirtschaft.
    77. Yingying DONG & Ying-Ying LEE & Michael GOU, 2019. "Regression Discontinuity Designs with a Continuous Treatment," Discussion papers 19058, Research Institute of Economy, Trade and Industry (RIETI).
    78. Jin Li & Ye Luo & Zigan Wang & Xiaowei Zhang, 2021. "Asymptotic Theory for IV-Based Reinforcement Learning with Potential Endogeneity," Papers 2103.04021, arXiv.org, revised Dec 2024.
    79. Liao, Yuan & Jiang, Wenxin, 2011. "Posterior consistency of nonparametric conditional moment restricted models," MPRA Paper 38700, University Library of Munich, Germany.
    80. Fève, Frédérique & Florens, Jean-Pierre, 2014. "Iterative algorithm for non parametric estimation of the instrumental variables quantiles," Economics Letters, Elsevier, vol. 123(3), pages 300-304.
    81. Christos Kollias & Suzanna Maria Paleologou & Panayiotis Tzeremes & Nickolaos Tzeremes, 2018. "The demand for military spending in Latin American countries," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 27(1), pages 1-17, December.
    82. Schennach, Susanne M., 2008. "Quantile Regression With Mismeasured Covariates," Econometric Theory, Cambridge University Press, vol. 24(4), pages 1010-1043, August.
    83. Pierre-Andre Chiappori & Ivana Komunjer & Dennis Kristensen, 2011. "Nonparametric Identification and Estimation of Transformation Models," CAM Working Papers 2011-01, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
    84. Caetano, Carolina & Rothe, Christoph & Yıldız, Neşe, 2016. "A discontinuity test for identification in triangular nonseparable models," Journal of Econometrics, Elsevier, vol. 193(1), pages 113-122.
    85. Hiroaki Kaido & Kaspar Wüthrich, 2018. "Decentralization estimators for instrumental variable quantile regression models," CeMMAP working papers CWP72/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    86. Breunig, Christoph, 2016. "Specification testing in nonparametric instrumental quantile regression," SFB 649 Discussion Papers 2016-032, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    87. Ian Crawford & Matthew Polisson, 2015. "Demand analysis with partially observed prices," IFS Working Papers W15/16, Institute for Fiscal Studies.
    88. Horowitz, Joel L., 2014. "Adaptive nonparametric instrumental variables estimation: Empirical choice of the regularization parameter," Journal of Econometrics, Elsevier, vol. 180(2), pages 158-173.
    89. Koki Fusejima, 2020. "Identification of multi-valued treatment effects with unobserved heterogeneity," Papers 2010.04385, arXiv.org, revised Apr 2023.
    90. Joachim Freyberger & Joel L. Horowitz, 2013. "Identification and shape restrictions in nonparametric instrumental variables estimation," CeMMAP working papers CWP31/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    91. Jinyong Hahn & Geert Ridder, 2011. "Conditional Moment Restrictions and Triangular Simultaneous Equations," The Review of Economics and Statistics, MIT Press, vol. 93(2), pages 683-689, May.
    92. Giovanni Forchini, 2014. "A General Result on Observational Equivalence in a Class of Nonparametric Structural Equations Models," School of Economics Discussion Papers 0114, School of Economics, University of Surrey.
    93. Arthur Lewbel, 2018. "The Identification Zoo - Meanings of Identification in Econometrics," Boston College Working Papers in Economics 957, Boston College Department of Economics, revised 14 Dec 2019.
    94. Maximilian Kasy, 2014. "Instrumental Variables with Unrestricted Heterogeneity and Continuous Treatment," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(4), pages 1614-1636.
    95. Kaspar W thrich, 2015. "Semiparametric estimation of quantile treatment effects with endogeneity," Diskussionsschriften dp1509, Universitaet Bern, Departement Volkswirtschaft.
    96. Halbert White & Karim Chalak, 2008. "Identifying Structural Effects in Nonseparable Systems Using Covariates," Boston College Working Papers in Economics 734, Boston College Department of Economics.
    97. Rothe, Christoph, 2010. "Identification of unconditional partial effects in nonseparable models," Economics Letters, Elsevier, vol. 109(3), pages 171-174, December.

  63. Victor Chernozhukov & Han Hong & Elie Tamer, 2007. "Estimation and Confidence Regions for Parameter Sets in Econometric Models," Econometrica, Econometric Society, vol. 75(5), pages 1243-1284, September.

    Cited by:

    1. Hiroaki Kaido, 2014. "Asymptotically efficient estimation of weighted average derivatives with an interval censored variable," CeMMAP working papers 03/14, Institute for Fiscal Studies.
    2. Atsushi Inoue & Mototsugu Shintani, 2018. "Quasi‐Bayesian model selection," Quantitative Economics, Econometric Society, vol. 9(3), pages 1265-1297, November.
    3. Andres Aradillas-Lopez & Adam Rosen, 2013. "Inference in ordered response games with complete information," CeMMAP working papers 33/13, Institute for Fiscal Studies.
    4. Christopher P. Chambers & Federico Echenique & Nicolas Lambert, 2019. "Recovering Preferences from Finite Data," Papers 1909.05457, arXiv.org, revised Oct 2020.
    5. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Testing many moment inequalities," CeMMAP working papers CWP42/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Ulrich K. Müller & Andriy Norets, 2016. "Coverage Inducing Priors in Nonstandard Inference Problems," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1233-1241, July.
    7. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    8. Sam Cosaert & Thomas Demuynck, 2018. "Nonparametric welfare and demand analysis with unobserved individual heterogeneity," ULB Institutional Repository 2013/251988, ULB -- Universite Libre de Bruxelles.
    9. Patrick Bajari & Jeremy T. Fox & Stephen Ryan, 2006. "Evaluating Wireless Carrier Consolidation Using Semiparametric Demand Estimation," NBER Working Papers 12425, National Bureau of Economic Research, Inc.
    10. Stoye, Jörg & Kitamura, Yuichi, 2013. "Nonparametric Analysis of Random Utility Models: Testing," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79753, Verein für Socialpolitik / German Economic Association.
    11. Menzel, Konrad, 2014. "Consistent estimation with many moment inequalities," Journal of Econometrics, Elsevier, vol. 182(2), pages 329-350.
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    2. Patricks Ogiji & Tersoo Shimonkabir Shitile & Nuruddeen Usman, 2022. "Estimating asymmetries in monetary policy reaction function: an oil price augmented Taylor type rule for Nigeria under unconventional regime," Economic Change and Restructuring, Springer, vol. 55(3), pages 1655-1672, August.
    3. Romuald Meango & Esther Mirjam Girsberger, 2023. "Identification of Ex ante Returns Using Elicited Choice Probabilities: an Application to Preferences for Public-sector Jobs," Papers 2303.03009, arXiv.org, revised Jun 2024.
    4. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2006. "Nonparametric Tests for Treatment Effect Heterogeneity," NBER Technical Working Papers 0324, National Bureau of Economic Research, Inc.
    5. Galvao, Antonio F. & Montes-Rojas, Gabriel, 2015. "On the equivalence of instrumental variables estimators for linear models," Economics Letters, Elsevier, vol. 134(C), pages 13-15.
    6. Yu-Chin Hsu & Ta-Cheng Huang & Haiqing Xu, 2018. "Testing for Unobserved Heterogeneous Treatment Effects with Observational Data," Papers 1803.07514, arXiv.org, revised Aug 2021.
    7. Bang, James T. & Mitra, Aniruddha & Wunnava, Phanindra V., 2018. "Hollowing Out the Middle? Remittances and Income Inequality in Nigeria," IZA Discussion Papers 11438, Institute of Labor Economics (IZA).
    8. Xu, Xiu & Wang, Weining & Shin, Yongcheol, 2020. "Dynamic Spatial Network Quantile Autoregression," IRTG 1792 Discussion Papers 2020-024, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    9. Isaiah Andrews & Anna Mikusheva, 2016. "Conditional Inference With a Functional Nuisance Parameter," Econometrica, Econometric Society, vol. 84, pages 1571-1612, July.
    10. Lingjie Ma & Roger Koenker, 2004. "Quantile regression methods for recursive structural equation models," CeMMAP working papers CWP01/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Yingyao Hu & Susanne M. Schennach, 2008. "Instrumental Variable Treatment of Nonclassical Measurement Error Models," Econometrica, Econometric Society, vol. 76(1), pages 195-216, January.
    12. Martinez-Sanchis, Elena & Mora, Juan & Kandemir, Ilker, 2012. "Counterfactual distributions of wages via quantile regression with endogeneity," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3212-3229.
    13. Horowitz, Joel L. & Lee, Sokbae, 2009. "Testing a parametric quantile-regression model with an endogenous explanatory variable against a nonparametric alternative," Journal of Econometrics, Elsevier, vol. 152(2), pages 141-152, October.
    14. Tae-Hwan Kim & Christophe Muller, 2020. "Inconsistency transmission and variance reduction in two-stage quantile regression," Post-Print hal-02084505, HAL.
    15. Alberto Díaz Dapena & Esteban Fernández Vázquez & Fernando Rubiera Morollón, 2015. "Estimating agglomeration economies in Spain: evidence from geographically disaggregated data," ERSA conference papers ersa15p285, European Regional Science Association.
    16. Andrew Chesher, 2007. "Endogeneity and discrete outcomes," CeMMAP working papers CWP05/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    17. Brantly Callaway & Tong Li, 2019. "Quantile treatment effects in difference in differences models with panel data," Quantitative Economics, Econometric Society, vol. 10(4), pages 1579-1618, November.
    18. Younes Ben Zaied & Béchir Ben Lahouel, 2021. "Does environmental CSR performance matter for corporate financial performance? Evidence from panel quantile regression," Economics Bulletin, AccessEcon, vol. 41(3), pages 938-951.
    19. Nguezet, Paul Martin Dontsop & Diagne, Aliou & Okoruwa, Victor Olusegun & Ojehomon, Vivian, 2011. "Impact of Improved Rice Technology (NERICA varieties) on Income and Poverty among Rice Farming Households in Nigeria: A Local Average Treatment Effect (LATE) Approach," Quarterly Journal of International Agriculture, Humboldt-Universitaat zu Berlin, vol. 50(3), pages 1-25.
    20. Haruki Kono, 2024. "Local Identification in Instrumental Variable Multivariate Quantile Regression Models," Papers 2401.11422, arXiv.org, revised Jun 2024.
    21. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey & Sami Stouli & Francis Vella, 2017. "Semiparametric estimation of structural functions in nonseparable triangular models," CeMMAP working papers CWP48/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    22. Neng-Chieh Chang, 2020. "The Mode Treatment Effect," Papers 2007.11606, arXiv.org.
    23. Nicholas C.S. Sim, 2009. "Modeling Quantile Dependence: A New Look at the Money-Output Relationship," School of Economics and Public Policy Working Papers 2009-34, University of Adelaide, School of Economics and Public Policy.
    24. Sasaki, Yuya, 2015. "What Do Quantile Regressions Identify For General Structural Functions?," Econometric Theory, Cambridge University Press, vol. 31(5), pages 1102-1116, October.
    25. Matthew A. Masten & Alexandre Poirier, 2018. "Interpreting Quantile Independence," Papers 1804.10957, arXiv.org.
    26. Stillman, Steven & McKenzie, David & Gibson, John, 2007. "Migration and mental health : evidence from a natural experiment," Policy Research Working Paper Series 4138, The World Bank.
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    3. Tong Li & Xiaoyong Zheng, 2006. "Entry and competition effects in first-price auctions: theory and evidence from procurement auctions," CeMMAP working papers CWP13/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    8. McCrary, Justin, 2008. "Manipulation of the running variable in the regression discontinuity design: A density test," Journal of Econometrics, Elsevier, vol. 142(2), pages 698-714, February.
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    1. Horowitz, Joel L. & Lee, Sokbae, 2009. "Testing a parametric quantile-regression model with an endogenous explanatory variable against a nonparametric alternative," Journal of Econometrics, Elsevier, vol. 152(2), pages 141-152, October.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    3. Javier Alejo & Antonio F Galvao & Gabriel Montes-Rojas, 2023. "A first-stage representation for instrumental variables quantile regression," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 350-377.
    4. Kaspar Wuthrich & Ying Zhu, 2019. "Omitted variable bias of Lasso-based inference methods: A finite sample analysis," Papers 1903.08704, arXiv.org, revised Sep 2021.
    5. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers 13/16, Institute for Fiscal Studies.
    6. Le-Yu Chen & Sokbae (Simon) Lee, 2017. "Exact computation of GMM estimators for instrumental variable quantile regression models," CeMMAP working papers CWP52/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Wehby, George L. & Courtemanche, Charles J., 2012. "The heterogeneity of the cigarette price effect on body mass index," Journal of Health Economics, Elsevier, vol. 31(5), pages 719-729.
    8. Timothy Besley & Neil Meads & Paolo Surico, 2012. "Risk Heterogeneity and Credit Supply: Evidence from the Mortgage Market," NBER Chapters, in: NBER Macroeconomics Annual 2012, Volume 27, pages 375-419, National Bureau of Economic Research, Inc.
    9. Susumu Imai & Hajime Katayama & Kala Krishna, 2008. "A Quantile-based Test of Protection for Sale Model," NBER Working Papers 13900, National Bureau of Economic Research, Inc.
    10. Hafemann, Lucas, 2023. "A house prices at risk approach for the German residential real estate market," Technical Papers 07/2023, Deutsche Bundesbank.
    11. Lin, Wei & Cai, Zongwu & Li, Zheng & Su, Li, 2015. "Optimal smoothing in nonparametric conditional quantile derivative function estimation," Journal of Econometrics, Elsevier, vol. 188(2), pages 502-513.
    12. Barceló, Cristina & Villanueva, Ernesto, 2016. "The response of household wealth to the risk of job loss: Evidence from differences in severance payments," Labour Economics, Elsevier, vol. 39(C), pages 35-54.
    13. Daniel Jacob, 2021. "CATE meets ML," Digital Finance, Springer, vol. 3(2), pages 99-148, June.
    14. Blaise Melly und Kaspar W thrich, 2016. "Local quantile treatment effects," Diskussionsschriften dp1605, Universitaet Bern, Departement Volkswirtschaft.
    15. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    16. Jau-er Chen & Chien-Hsun Huang & Jia-Jyun Tien, 2021. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
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    18. Victor Chernozhukov & Christian Hansen, 2013. "Quantile models with endogeneity," CeMMAP working papers CWP25/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    20. Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers 57/13, Institute for Fiscal Studies.
    21. Leo Kaas & Georgi Kocharkov & Edgar Preugschat, 2019. "Wealth Inequality and Homeownership in Europe," Annals of Economics and Statistics, GENES, issue 136, pages 27-54.
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    38. Chenhong Peng & Paul Siu Fai Yip & Yik Wa Law, 2019. "Intergenerational Earnings Mobility and Returns to Education in Hong Kong: A Developed Society with High Economic Inequality," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 143(1), pages 133-156, May.
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    44. Maria Giuseppina Bruna & Rey Đặng & Aymen Ammari & L'Hocine Houanti, 2021. "The effect of board gender diversity on corporate social performance: An instrumental variable quantile regression approach," Post-Print hal-04455603, HAL.
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    47. Kaspar W thrich, 2014. "A Comparison of two Quantile Models with Endogeneity," Diskussionsschriften dp1408, Universitaet Bern, Departement Volkswirtschaft.
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    50. GINSBURGH, Victor & PRIETO-RODRIGUEZ, Juan, 2013. "Is there a gender bias in the use of foreign languages in Europe?," LIDAM Reprints CORE 2573, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    51. George L. Wehby & Kwame A. Nyarko & Jorge S. Lopez‐Camelo, 2014. "Fetal Health Shocks And Early Inequalities In Health Capital Accumulation," Health Economics, John Wiley & Sons, Ltd., vol. 23(1), pages 69-92, January.
    52. George L. Wehby & Jeffrey C. Murray & Eduardo E. Castilla & Jorge S. Lopez‐Camelo & Robert L. Ohsfeldt, 2009. "Quantile effects of prenatal care utilization on birth weight in Argentina," Health Economics, John Wiley & Sons, Ltd., vol. 18(11), pages 1307-1321, November.
    53. Ping Yu & Peter C.B. Phillips, 2014. "Threshold Regression with Endogeneity," Cowles Foundation Discussion Papers 1966, Cowles Foundation for Research in Economics, Yale University.
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    55. DePaula, Guilherme, 2020. "The distributional effect of climate change on agriculture: Evidence from a Ricardian quantile analysis of Brazilian census data," Journal of Environmental Economics and Management, Elsevier, vol. 104(C).
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    1. Chen, Songnian, 2018. "Sequential estimation of censored quantile regression models," Journal of Econometrics, Elsevier, vol. 207(1), pages 30-52.
    2. Alina Ozhegova & Evgeniy M. Ozhegov, 2018. "Heterogeneity in demand for performances and seats in the theatre," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(3), pages 131-145, June.
    3. Erik Figueiredo & Luiz Renato Lima & Gianluca Orefice, 2016. "Migration and Regional Trade Agreements: A (New) Gravity Estimation," Review of International Economics, Wiley Blackwell, vol. 24(1), pages 99-125, February.
    4. Hartley, Robert Paul & Lamarche, Carlos, 2017. "Behavioral Responses and Welfare Reform: Evidence from a Randomized Experiment," IZA Discussion Papers 10905, Institute of Labor Economics (IZA).
    5. Mingli Chen & Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly, 2017. "Counterfactual analysis in R: a vignette," CeMMAP working papers 64/17, Institute for Fiscal Studies.
    6. Jesse Edgerton, 2011. "Agency problems in public firms: evidence from corporate jets in leveraged buyouts," Finance and Economics Discussion Series 2011-15, Board of Governors of the Federal Reserve System (U.S.).
    7. Bönke, Timm & Massarrat-Mashhadi, Nima & Sielaff, Christian, 2010. "Charitable giving in the German welfare state: Fiscal incentives and crowding out," Discussion Papers 2010/30, Free University Berlin, School of Business & Economics.
    8. Choi, Jin-young & Lee, Myoung-jae, 2019. "Twins are more different than commonly believed, but made less different by compensating behaviors," Economics & Human Biology, Elsevier, vol. 35(C), pages 18-31.
    9. Amanda Kowalski, 2016. "Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 107-117, January.
    10. Joshua Graff Zivin & Matthew Neidell, 2012. "The Impact of Pollution on Worker Productivity," American Economic Review, American Economic Association, vol. 102(7), pages 3652-3673, December.
    11. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly, 2012. "Inference on counterfactual distributions," CeMMAP working papers 05/12, Institute for Fiscal Studies.
    12. Alejo, Javier & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2018. "Quantile continuous treatment effects," Econometrics and Statistics, Elsevier, vol. 8(C), pages 13-36.
    13. Pradhan, Jaya Prakash & Zohair, Mohammad & Alagawadi, Mallikarjun V., 2012. "Regional Policies, Firm Characteristics, and Exporting in the Indian State of Karnataka," MPRA Paper 40717, University Library of Munich, Germany.
    14. Victor Chernozhukov & Ivan Fernández-Val & Sukjin Han & Amanda Kowalski, 2019. "Censored quantile instrumental-variable estimation with Stata," Stata Journal, StataCorp LP, vol. 19(4), pages 768-781, December.
    15. Diana Ayala & Milan Nedeljkoviæ & Christian Saborowski, 2015. "What Slice of the Pie? The Corporate Bond Market Boom in Emerging Economies," Working papers 30, National Bank of Serbia.
    16. Raslan Alzuabi & Sarah Brown & Daniel Gray & Mark N Harris & Christopher Spencer, 2022. "Household saving, health, and healthcare utilization in Japan [Stature, obesity, and portfolio choice]," Oxford Economic Papers, Oxford University Press, vol. 74(2), pages 473-497.
    17. Mukasa Adamon N. & Anthony M. Simpasa & Adeleke Oluwole Salami, 2017. "Working Paper 247 - Credit constraints and farm productivity: Micro-level evidence from smallholder farmers in Ethiopia," Working Paper Series 2356, African Development Bank.
    18. Marinho Bertanha & Andrew H. McCallum & Nathan Seegert, 2021. "Better Bunching, Nicer Notching," Finance and Economics Discussion Series 2021-002, Board of Governors of the Federal Reserve System (U.S.).
    19. Schmillen, Achim & Möller, Joachim, 2012. "Distribution and determinants of lifetime unemployment," Labour Economics, Elsevier, vol. 19(1), pages 33-47.
    20. Achim Schmillen & Joachim Möller, 2009. "Determinants of Lifetime Unemployment - A Micro Data Analysis with Censored Quantile Regressions," Working Papers 275, Leibniz Institut für Ost- und Südosteuropaforschung (Institute for East and Southeast European Studies).
    21. Santiago Acerenza & Vitor Possebom & Pedro H. C. Sant'Anna, 2023. "Was Javert right to be suspicious? Unpacking treatment effect heterogeneity of alternative sentences on time-to-recidivism in Brazil," Papers 2311.13969, arXiv.org, revised May 2024.
    22. Fernández-Val, Iván & van Vuuren, Aico & Vella, Francis & Peracchi, Franco, 2022. "Selection and the Distribution of Female Hourly Wages in the U.S," IZA Discussion Papers 15028, Institute of Labor Economics (IZA).
    23. Matthew Harding & Carlos Lamarche, 2018. "A Panel Quantile Approach to Attrition Bias in Big Data: Evidence from a Randomized Experiment," Papers 1808.03364, arXiv.org.
    24. Gabrielle Fack & Camille Landais, 2010. "Are Tax Incentives for Charitable Giving Efficient? Evidence from France," American Economic Journal: Economic Policy, American Economic Association, vol. 2(2), pages 117-141, May.
    25. Bönke, Timm & Jochimsen, Beate & Schröder, Carsten, 2011. "Fiscal equalization and regions' (un)willingness-to-tax: Evidence from Germany," Economics Working Papers 2011-06, Christian-Albrechts-University of Kiel, Department of Economics.
    26. Fan, Yanqin & Liu, Ruixuan, 2018. "Partial identification and inference in censored quantile regression," Journal of Econometrics, Elsevier, vol. 206(1), pages 1-38.
    27. Haiqing Xu, "undated". "Estimation of Discrete Games with Correlated Types," Department of Economics Working Papers 130909, The University of Texas at Austin, Department of Economics.
    28. Jad Beyhum & Lorenzo Tedesco & Ingrid Van Keilegom, 2022. "Instrumental variable quantile regression under random right censoring," Papers 2209.01429, arXiv.org, revised Feb 2023.
    29. Lin, Guixian & He, Xuming & Portnoy, Stephen, 2012. "Quantile regression with doubly censored data," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 797-812.
    30. Blundell, Richard & Powell, James L., 2007. "Censored regression quantiles with endogenous regressors," Journal of Econometrics, Elsevier, vol. 141(1), pages 65-83, November.
    31. Chen, Songnian, 2019. "Quantile regression for duration models with time-varying regressors," Journal of Econometrics, Elsevier, vol. 209(1), pages 1-17.
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    33. Victor Chernozhukov & Ivan Fernandez-Val & Amanda Kowalski, 2011. "Quantile Regression with Censoring and Endogeneity," Cowles Foundation Discussion Papers 1797, Cowles Foundation for Research in Economics, Yale University.
    34. Fitzenberger, Bernd & Wilke, Ralf A., 2005. "Using Quantile Regression for Duration Analysis," ZEW Discussion Papers 05-65, ZEW - Leibniz Centre for European Economic Research.
    35. Lei Chen & Rangan Gupta & Zinnia Mukherjee & Peter Wanke, 2015. "Technical Efficiency of Connecticut Long Island Sound Lobster Fishery: A Nonparametric Approach to Aggregate Frontier Analysis," Working Papers 201578, University of Pretoria, Department of Economics.
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    66. Wang, Chuan-Sheng & Zhao, Zhibiao, 2016. "Conditional Value-at-Risk: Semiparametric estimation and inference," Journal of Econometrics, Elsevier, vol. 195(1), pages 86-103.
    67. Shige Peng & Shuzhen Yang & Jianfeng Yao, 2018. "Improving Value-at-Risk prediction under model uncertainty," Papers 1805.03890, arXiv.org, revised Jun 2020.
    68. He, Xuming & Pan, Xiaoou & Tan, Kean Ming & Zhou, Wen-Xin, 2023. "Smoothed quantile regression with large-scale inference," Journal of Econometrics, Elsevier, vol. 232(2), pages 367-388.
    69. Lee, Tae-Hwy & Yang, Yang, 2006. "Bagging binary and quantile predictors for time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 465-497.
    70. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    71. Rubia, Antonio & Sanchis-Marco, Lidia, 2013. "On downside risk predictability through liquidity and trading activity: A dynamic quantile approach," International Journal of Forecasting, Elsevier, vol. 29(1), pages 202-219.
    72. Di, Junpeng & Zhu, Pingfang, 2015. "A method for evaluating the extreme risk sources of financial markets: The case of stock markets in China," Global Finance Journal, Elsevier, vol. 26(C), pages 18-28.
    73. Giessing, Alexander & He, Xuming, 2019. "On the predictive risk in misspecified quantile regression," Journal of Econometrics, Elsevier, vol. 213(1), pages 235-260.

Software components

  1. Victor Chernozhukov & Ivan Fernandez-Val & Sukjin Han & Amanda Kowalski, 2012. "CQIV: Stata module to perform censored quantile instrumental variables regression," Statistical Software Components S457478, Boston College Department of Economics, revised 25 Sep 2019.

    Cited by:

    1. Martin Falk & Eva Hagsten, 2014. "Output growth and prices of establishments in the Swedish lodging industry," ERSA conference papers ersa14p360, European Regional Science Association.
    2. Gabrielle Fack & Camille Landais, 2016. "The effect of tax enforcement on tax elasticities: Evidence from charitable contributions in France," PSE-Ecole d'économie de Paris (Postprint) hal-01300122, HAL.
    3. Mendolia, Silvia & Paloyo, Alfredo R. & Walker, Ian, 2016. "Heterogeneous effects of high school peers on educational outcomes," Ruhr Economic Papers 612, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    4. Richard T. Melstrom & Deshamithra H. W. Jayasekera, 2017. "Two-Stage Estimation to Control for Unobservables in a Recreation Demand Model with Unvisited Sites," Land Economics, University of Wisconsin Press, vol. 93(2), pages 328-341.
    5. Sven Neelsen & Owen O'Donnell, 2017. "Progressive universalism? The impact of targeted coverage on health care access and expenditures in Peru," Health Economics, John Wiley & Sons, Ltd., vol. 26(12), pages 179-203, December.
    6. Broberg, Thomas & Kazukauskas, Andrius, 2014. "Inefficiencies in residential use of energy - A critical overview of literature and energy efficiency policies in EU and Sweden," CERE Working Papers 2014:7, CERE - the Center for Environmental and Resource Economics.
    7. Kiran Krishnamurthy, Chandra & Kriström, Bengt, 2013. "Determinants of the price-premium for Green Energy: Evidence from an OECD cross-section," CERE Working Papers 2013:7, CERE - the Center for Environmental and Resource Economics, revised 30 Jun 2014.
    8. Michael J. Peel, 2014. "Addressing unobserved endogeneity bias in accounting studies: control and sensitivity methods by variable type," Accounting and Business Research, Taylor & Francis Journals, vol. 44(5), pages 545-571, October.

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