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Inference for High-Dimensional Sparse Econometric Models

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Cited by:

  1. 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.
  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. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
  4. 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.
  5. 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.
  6. 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.
  7. Ning Xu & Jian Hong & Timothy C. G. Fisher, 2016. "Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso," Papers 1606.00142, arXiv.org.
  8. 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.
  9. 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.
  10. Demian Pouzo, 2015. "On the Non-Asymptotic Properties of Regularized M-estimators," Papers 1512.06290, arXiv.org, revised Oct 2016.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. de Paula, Aureo & Rasul, Imran & Souza, Pedro, 2018. "Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition," CEPR Discussion Papers 12792, C.E.P.R. Discussion Papers.
  16. 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.
  17. Scoles, Brooke & Nicodemo, Catia, 2022. "Doctors’ attitudes toward specific medical conditions," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 182-199.
  18. Gold, David & Lederer, Johannes & Tao, Jing, 2020. "Inference for high-dimensional instrumental variables regression," Journal of Econometrics, Elsevier, vol. 217(1), pages 79-111.
  19. 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.
  20. Matteo Barigozzi & Christian Brownlees, 2019. "NETS: Network estimation for time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(3), pages 347-364, April.
  21. Belloni, Alexandre. & Chen, Mingli & Chernozhukov, Victor, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Financial Risk Management," The Warwick Economics Research Paper Series (TWERPS) 1125, University of Warwick, Department of Economics.
  22. Lim, Dennis & Wang, Wenjie & Zhang, Yichong, 2024. "A conditional linear combination test with many weak instruments," Journal of Econometrics, Elsevier, vol. 238(2).
  23. 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.
  24. 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.
  25. Mueller, Hannes & Rauh, Christopher, 2018. "Reading Between the Lines: Prediction of Political Violence Using Newspaper Text," American Political Science Review, Cambridge University Press, vol. 112(2), pages 358-375, May.
  26. 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.
  27. 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.
  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. 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.
  30. Sander Gerritsen & Mark Kattenberg & Sonny Kuijpers, 2019. "The impact of age at arrival on education and mental health," CPB Discussion Paper 389.rdf, CPB Netherlands Bureau for Economic Policy Analysis.
  31. Myrto Kalouptsidi, 2014. "Time to Build and Fluctuations in Bulk Shipping," American Economic Review, American Economic Association, vol. 104(2), pages 564-608, February.
  32. 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).
  33. 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.
  34. Li, Zhaoyuan & Yao, Jianfeng, 2019. "Testing for heteroscedasticity in high-dimensional regressions," Econometrics and Statistics, Elsevier, vol. 9(C), pages 122-139.
  35. Alec Smith & B. Douglas Bernheim & Colin F. Camerer & Antonio Rangel, 2014. "Neural Activity Reveals Preferences without Choices," American Economic Journal: Microeconomics, American Economic Association, vol. 6(2), pages 1-36, May.
  36. Kock, Anders Bredahl & Callot, Laurent, 2015. "Oracle inequalities for high dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 186(2), pages 325-344.
  37. Song Song & Peter J. Bickel, 2011. "Large Vector Auto Regressions," Papers 1106.3915, arXiv.org.
  38. 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).
  39. 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.
  40. 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.
  41. Matthew Gentzkow & Bryan T. Kelly & Matt Taddy, 2017. "Text as Data," NBER Working Papers 23276, National Bureau of Economic Research, Inc.
  42. 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.
  43. 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.
  44. Gambacorta, Leonardo & Huang, Yiping & Qiu, Han & Wang, Jingyi, 2024. "How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm," Journal of Financial Stability, Elsevier, vol. 73(C).
  45. 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.
  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. 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).
  48. 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.
  49. 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.
  50. 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.
  51. Victor Chernozhukov & Chris Hansen & Martin Spindler, 2016. "High-Dimensional Metrics in R," Papers 1603.01700, arXiv.org, revised Aug 2016.
  52. Riccardo Di Francesco, 2023. "Ordered Correlation Forest," Papers 2309.08755, arXiv.org.
  53. 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.
  54. 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.
  55. Florens, Jean-Pierre & Van Bellegem, Sébastien, 2015. "Instrumental variable estimation in functional linear models," Journal of Econometrics, Elsevier, vol. 186(2), pages 465-476.
  56. 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.
  57. 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.
  58. 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).
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