IDEAS home Printed from https://ideas.repec.org/e/c/pwt1.html
   My authors  Follow this author

Kaspar Wüthrich
(Kaspar Wuthrich)

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.

Working papers

  1. Davide Viviano & Kaspar Wuthrich & Paul Niehaus, 2021. "A model of multiple hypothesis testing," Papers 2104.13367, arXiv.org, revised Apr 2024.

    Cited by:

    1. 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.
    2. Jeffrey D. Michler & Anna Josephson, 2021. "Recent Developments in Inference: Practicalities for Applied Economics," Papers 2107.09736, arXiv.org.

  2. Grigory Franguridi & Bulat Gafarov & Kaspar Wüthrich, 2021. "Conditional Quantile Estimators: A Small Sample Theory," CESifo Working Paper Series 9046, CESifo.

    Cited by:

    1. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.

  3. Wüthrich, Kaspar, 2020. "A Comparison of Two Quantile Models With Endogeneity," University of California at San Diego, Economics Working Paper Series qt0q43931f, Department of Economics, UC San Diego.

    Cited by:

    1. 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".
    2. Grigory Franguridi & Bulat Gafarov & Kaspar Wuthrich, 2020. "Bias correction for quantile regression estimators," Papers 2011.03073, arXiv.org, revised Nov 2024.
    3. Alecos Papadopoulos & Christopher F. Parmeter, 2022. "Quantile Methods for Stochastic Frontier Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 12(1), pages 1-120, November.
    4. Grigory Franguridi & Bulat Gafarov & Kaspar Wüthrich, 2021. "Conditional Quantile Estimators: A Small Sample Theory," CESifo Working Paper Series 9046, CESifo.
    5. Jad Beyhum & Jean-Pierre Florens & Ingrid Keilegom, 2023. "A nonparametric instrumental approach to confounding in competing risks models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 709-734, October.
    6. Kaspar W thrich, 2015. "Semiparametric estimation of quantile treatment effects with endogeneity," Diskussionsschriften dp1509, Universitaet Bern, Departement Volkswirtschaft.
    7. 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).
    8. 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.

  4. 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.
    4. Wenjie Wang & Yichong Zhang, 2021. "Wild Bootstrap for Instrumental Variables Regressions with Weak and Few Clusters," Papers 2108.13707, arXiv.org, revised Jan 2024.
    5. Elisa Toledo & Wilman Santiago Ochoa-Moreno & Rafael Alvarado & Lizeth Cuesta & Muntasir Murshed & Abdul Rehman, 2022. "Forest Area: Old and New Factors That Affect Its Dynamics," Sustainability, MDPI, vol. 14(7), pages 1-17, March.
    6. 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.

  5. Chernozhukov, Victor & Fernández-Val, Iván & Melly, Blaise & Wüthrich, Kaspar, 2020. "Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes," University of California at San Diego, Economics Working Paper Series qt5zm6m9rq, Department of Economics, UC San Diego.

    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.

  6. Karthik Muralidharan & Mauricio Romero & Kaspar Wüthrich, 2020. "Factorial Designs, Model Selection, and (Incorrect) Inference in Randomized Experiments," CESifo Working Paper Series 8137, CESifo.

    Cited by:

    1. Andres Gonzalez-Lira & Ahmed Mushfiq Mobarak, 2021. "Slippery Fish: Enforcing Regulation when Agents Learn and Adapt," NBER Working Papers 28610, National Bureau of Economic Research, Inc.
    2. Sokbae Lee & Bernard Salani'e, 2020. "Treatment Effects with Targeting Instruments," Papers 2007.10432, arXiv.org, revised Dec 2024.
    3. Leaver,Clare & Ozier,Owen & Serneels,Pieter Maria & Zeitlin,Andrew, 2020. "Recruitment, Effort, and Retention Effects of Performance Contracts for Civil Servants : Experimental Evidence from Rwandan Primary Schools," Policy Research Working Paper Series 9395, The World Bank.
    4. Michael Grimm & Renate Hartwig, 2022. "All eyes on the price: An assessment of the willingness‐to‐pay for eyeglasses in rural Burkina Faso," Health Economics, John Wiley & Sons, Ltd., vol. 31(7), pages 1347-1367, July.
    5. Shah,Manisha & Seager,Jennifer & Montalvao Machado,Joao H. C. & Goldstein,Markus P., 2022. "Two Sides of Gender : Sex, Power, and Adolescence," Policy Research Working Paper Series 10072, The World Bank.
    6. Daniel Engler & Gunnar Gutsche & Amantia Simixhiu & Andreas Ziegler, 2022. "Social norms and individual climate protection activities: A framed field experiment for Germany," MAGKS Papers on Economics 202230, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    7. 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).
    8. Derksen, Laura & Kerwin, Jason & Reynoso, Natalia Ordaz & Sterck, Olivier, 2024. "Healthcare Appointments as Commitment Devices," IZA Discussion Papers 17070, Institute of Labor Economics (IZA).
    9. Jorge Luis García & James J. Heckman, 2020. "Early Childhood Education and Life-cycle Health," NBER Working Papers 26880, National Bureau of Economic Research, Inc.
    10. Erika Deserranno & Philipp Kastrau & Gianmarco León-Ciliotta, 2021. "Promotions and productivity: The role of meritocracy and pay progression in the public sector," Economics Working Papers 1770, Department of Economics and Business, Universitat Pompeu Fabra.
    11. Philipp Ketz & Adam Mccloskey, 2024. "Short and Simple Confidence Intervals When the Directions of Some Effects Are Known," Working Papers hal-03388199, HAL.
    12. Fernando, A. Nilesh, 2021. "Seeking the treated: The impact of mobile extension on farmer information exchange in India," Journal of Development Economics, Elsevier, vol. 153(C).
    13. Laura Derksen & Jason Kerwin & Natalia Ordaz Reynoso & Olivier Sterck, 2021. "Appointments: A More Effective Commitment Device for Health Behaviors," Papers 2110.06876, arXiv.org.
    14. Girija Bahety & Sebastian Bauhoff & Dev Patel & James Potter, 2021. "Texts Don’t Nudge: An Adaptive Trial to Prevent the Spread of COVID-19 in India," Working Papers 585, Center for Global Development.
    15. Deserranno, Erika & Leon-Ciliotta, Gianmarco, 2022. "Promotions and Productivity: The Role of Meritocracy and Pay Progression in the Public Sector," CEPR Discussion Papers 15837, C.E.P.R. Discussion Papers.
    16. Seim, Brigitte & Jablonski, Ryan & Ahlbäck, Johan, 2020. "How information about foreign aid affects public spending decisions: Evidence from a field experiment in Malawi," Journal of Development Economics, Elsevier, vol. 146(C).
    17. Baird, Sarah & McIntosh, Craig & Özler, Berk & Pape, Utz, 2024. "Asset transfers and anti-poverty programs: Experimental evidence from Tanzania," Journal of Development Economics, Elsevier, vol. 166(C).
    18. Timothy B. Armstrong & Michal Kolesár & Soonwoo Kwon, 2020. "Bias-Aware Inference in Regularized Regression Models," Working Papers 2020-2, Princeton University. Economics Department..
    19. M. Mehrab Bakhtiar & Raymond Guiteras & James A. Levinsohn & Ahmed Mushfiq Mobarak, 2021. "Social and Financial Incentives for Overcoming a Collective Action Problem," NBER Working Papers 29294, National Bureau of Economic Research, Inc.
    20. Toshi H. Arimura & Elke D. Groh & Miwa Nakai & Andreas Ziegler, 2022. "The causal effect of private and organizational climate-related identity on climate protection activities: Evidence from a framed field experiment in Japan," MAGKS Papers on Economics 202229, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    21. Seim, Brigitte & Jablonski, Ryan S. & Ahlback, Johan, 2020. "How information about foreign aid affects public spending decisions: evidence from a field experiment in Malawi," LSE Research Online Documents on Economics 105255, London School of Economics and Political Science, LSE Library.
    22. Kondylis,Florence & Loeser,John Ashton, 2021. "Intervention Size and Persistence," Policy Research Working Paper Series 9769, The World Bank.
    23. Engler, Daniel & Ziegler, Andreas & Gutsche, Gunnar & Simixhiu, Amantia, 2023. "Social Norms and Individual Climate Protection Activities: A Framed Field Experiment for Germany," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277662, Verein für Socialpolitik / German Economic Association.
    24. Weigel, Jonathan & Balán, Pablo & Bergeron, Augustin & Tourek, Gabriel, 2020. "Local Elites as State Capacity: How City Chiefs Use Local Information to Increase Tax Compliance in the D.R. Congo," CEPR Discussion Papers 15138, C.E.P.R. Discussion Papers.
    25. Macours, Karen & Behaghel, Luc & Gignoux, Jérémie, 2020. "Social learning in agriculture: does smallholder heterogeneity impede technology diffusion in Sub-Saharan Africa?," CEPR Discussion Papers 15220, C.E.P.R. Discussion Papers.
    26. Fang, Ximeng & Goette, Lorenz & Rockenbach, Bettina & Sutter, Matthias & Tiefenbeck, Verena & Schoeb, Samuel & Staake, Thorsten, 2023. "Complementarities in behavioral interventions: Evidence from a field experiment on resource conservation," Journal of Public Economics, Elsevier, vol. 228(C).
    27. Davide Viviano & Kaspar Wuthrich & Paul Niehaus, 2021. "A model of multiple hypothesis testing," Papers 2104.13367, arXiv.org, revised Apr 2024.
    28. Yuehao Bai & Jizhou Liu & Max Tabord-Meehan, 2022. "Inference for Matched Tuples and Fully Blocked Factorial Designs," Papers 2206.04157, arXiv.org, revised Nov 2023.

  7. Niklas Potrafke & Fabian Ruthardt & Kaspar Wüthrich, 2020. "Protectionism and Economic Growth: Causal Evidence from the First Era of Globalization," CESifo Working Paper Series 8759, CESifo.

    Cited by:

    1. Niklas Potrafke & Kaspar Wüthrich, 2020. "Green Governments," CESifo Working Paper Series 8726, CESifo.
    2. Keliang Wang & Bin Zhao & Tianzheng Fan & Jinning Zhang, 2022. "Economic Growth Targets and Carbon Emissions: Evidence from China," IJERPH, MDPI, vol. 19(13), pages 1-16, June.
    3. Bajo-Rubio, Oscar & Ramos-Herrera, María del Carmen, 2023. "Does international trade promote economic growth? Europe, 19th and 20th centuries," GLO Discussion Paper Series 1358, Global Labor Organization (GLO).

  8. Niklas Potrafke & Kaspar Wuthrich, 2020. "Green governments," Papers 2012.09906, arXiv.org, revised Mar 2022.

    Cited by:

    1. Wifo, 2021. "WIFO-Monatsberichte, Heft 1/2021," WIFO Monatsberichte (monthly reports), WIFO, vol. 94(1), January.
    2. Daniela Kletzan-Slamanig & Franz Sinabell, 2021. "Der Beitrag der Konjunkturbelebung zur Transformation. Einordnung von Maßnahmen der Bundesländer," WIFO Monatsberichte (monthly reports), WIFO, vol. 94(1), pages 67-78, January.

  9. Grigory Franguridi & Bulat Gafarov & Kaspar Wuthrich, 2020. "Bias correction for quantile regression estimators," Papers 2011.03073, arXiv.org, revised Nov 2024.

    Cited by:

    1. Yong Bao & Aman Ullah, 2021. "Analytical Finite Sample Econometrics: From A. L. Nagar to Now," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 17-37, December.

  10. 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.

    Cited by:

    1. 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.
    2. Khudri, Md Mohsan & Hussey, Andrew, 2024. "Breastfeeding and Child Development Outcomes across Early Childhood and Adolescence: Doubly Robust Estimation with Machine Learning," IZA Discussion Papers 17080, Institute of Labor Economics (IZA).
    3. Joshua Angrist, 2022. "Empirical Strategies in Economics: Illuminating the Path from Cause to Effect," NBER Working Papers 29726, National Bureau of Economic Research, Inc.
    4. Masayuki Hirukawa & Di Liu & Irina Murtazashvili & Artem Prokhorov, 2023. "DS-HECK: double-lasso estimation of Heckman selection model," Empirical Economics, Springer, vol. 64(6), pages 3167-3195, June.
    5. 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.

  11. 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.

  12. Graham Elliott & Nikolay Kudrin & Kaspar Wuthrich, 2019. "Detecting p-hacking," Papers 1906.06711, arXiv.org, revised May 2021.

    Cited by:

    1. Guido W. Imbens, 2021. "Statistical Significance, p-Values, and the Reporting of Uncertainty," Journal of Economic Perspectives, American Economic Association, vol. 35(3), pages 157-174, Summer.
    2. Josef Bajzik & Jan Janku & Simona Malovana & Klara Moravcova & Ngoc Anh Ngo, 2023. "Monetary Policy Has a Long-Lasting Impact on Credit: Evidence from 91 VAR Studies," Working Papers 2023/19, Czech National Bank.
    3. Simona Malovana & Martin Hodula & Zuzana Gric & Josef Bajzik, 2022. "Borrower-Based Macroprudential Measures and Credit Growth: How Biased is the Existing Literature?," Working Papers 2022/8, Czech National Bank.

  13. Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," University of California at San Diego, Economics Working Paper Series qt99n9197q, Department of Economics, UC San Diego.

    Cited by:

    1. Brantly Callaway, 2020. "Bounds on Distributional Treatment Effect Parameters using Panel Data with an Application on Job Displacement," Papers 2008.08117, arXiv.org.
    2. 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".
    3. Fusejima, Koki, 2024. "Identification of multi-valued treatment effects with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 238(1).
    4. 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.
    5. Grigory Franguridi & Bulat Gafarov & Kaspar Wüthrich, 2021. "Conditional Quantile Estimators: A Small Sample Theory," CESifo Working Paper Series 9046, CESifo.
    6. Afrouz Azadikhah Jahromi & Brantly Callaway, 2019. "Heterogeneous Effects of Job Displacement on Earnings," DETU Working Papers 1901, Department of Economics, Temple University.
    7. 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.

  14. 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.

    Cited by:

    1. 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.
    2. Holger Herz & Deborah Kistler & Christian Zehnder & Christian Zihlmann, 2022. "Hindsight Bias and Trust in Government," CESifo Working Paper Series 9767, CESifo.
    3. Berno Buechel & Selina Gangl & Martin Huber, 2021. "How residence permits affect the labor market attachment of foreign workers: Evidence from a migration lottery in Liechtenstein," Papers 2105.11840, arXiv.org.
    4. 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.
    5. 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.
    6. Herz, Holger & Kistler, Deborah & Zehnder, Christian & Zihlmann, Christian, 2022. "Hindsight Bias and Trust in Government: Evidence from the United States," FSES Working Papers 526, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    7. 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).
    8. Öberg, Stefan, 2021. "Treatment for natural experiments: How to improve causal estimates using conceptual definitions and substantive interpretations," SocArXiv pkyue, Center for Open Science.
    9. Martin Huber, 2024. "An Introduction to Causal Discovery," Papers 2407.08602, arXiv.org.

  15. 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.

  16. 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.

  17. Hiroaki Kaido & Kaspar Wuthrich, 2018. "Decentralization Estimators for Instrumental Variable Quantile Regression Models," Papers 1812.10925, arXiv.org, revised Sep 2020.

    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. Grigory Franguridi & Bulat Gafarov & Kaspar Wuthrich, 2020. "Bias correction for quantile regression estimators," Papers 2011.03073, arXiv.org, revised Nov 2024.
    3. Fusejima, Koki, 2024. "Identification of multi-valued treatment effects with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 238(1).
    4. Grigory Franguridi & Bulat Gafarov & Kaspar Wüthrich, 2021. "Conditional Quantile Estimators: A Small Sample Theory," CESifo Working Paper Series 9046, CESifo.
    5. Xin Liu, 2019. "Averaging estimation for instrumental variables quantile regression," Papers 1910.04245, arXiv.org.
    6. Wenjie Wang & Yichong Zhang, 2024. "Gradient Wild Bootstrap for Instrumental Variable Quantile Regressions with Weak and Few Clusters," Papers 2408.10686, arXiv.org.
    7. Wenjie Wang & Yichong Zhang, 2021. "Wild Bootstrap for Instrumental Variables Regressions with Weak and Few Clusters," Papers 2108.13707, arXiv.org, revised Jan 2024.
    8. 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.
    9. Koki Fusejima, 2020. "Identification of multi-valued treatment effects with unobserved heterogeneity," Papers 2010.04385, arXiv.org, revised Apr 2023.
    10. Lorenzo Tedesco & Jad Beyhum & Ingrid Van Keilegom, 2023. "Instrumental variable estimation of the proportional hazards model by presmoothing," Papers 2309.02183, arXiv.org.
    11. 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.

  18. 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.

  19. Blaise Melly und Kaspar W thrich, 2016. "Local quantile treatment effects," Diskussionsschriften dp1605, Universitaet Bern, Departement Volkswirtschaft.

    Cited by:

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

  20. Daniel Burkhard & Christian Schmid & Kaspar W thrich, 2015. "Financial incentives and physician prescription behavior: Evidence from dispensing regulations," Diskussionsschriften dp1511, Universitaet Bern, Departement Volkswirtschaft.

    Cited by:

    1. Rachamin, Yael & Meier, Rahel & Valeri, Fabio & Rosemann, Thomas & Muheim, Leander, 2021. "Physician-dispensing as a determinant of clinical and process measurements in patients at increased cardiovascular risk: A cross-sectional study in Swiss general practice," Health Policy, Elsevier, vol. 125(10), pages 1305-1310.
    2. Hjalmarsson, Linn & Schmid, Christian P.R. & Schreiner, Nicolas, 2024. "A Prescription for Knowledge: Patient Information and Generic Substitution," Working papers 2024/05, Faculty of Business and Economics - University of Basel.
    3. Gerfin, Michael & Müller, Tobias & Schmid, Christian, 2022. "Rents for Pills: Financial Incentives and Physician Behavior," VfS Annual Conference 2022 (Basel): Big Data in Economics 264037, Verein für Socialpolitik / German Economic Association.
    4. Alexander Ahammer & Ivan Zilic, 2017. "Do Financial Incentives Alter Physician Prescription Behavior? Evidence From Random Patient-GP Allocations," Economics working papers 2017-02, Department of Economics, Johannes Kepler University Linz, Austria.
    5. Boris Kaiser, 2017. "Gender-specific practice styles and ambulatory health care expenditures," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 18(9), pages 1157-1179, December.
    6. Fabienne Loetscher, Christian P.R. Schmid, Michael Gerfin, "undated". "The effect of higher out-of-pocket payments on drug prices and demand," Diskussionsschriften dp2405, Universitaet Bern, Departement Volkswirtschaft.
    7. Olivia Bodnar & Hugh Gravelle & Nils Gutacker & Annika Herr, 2021. "Financial incentives and prescribing behaviour in primary care," Working Papers 181cherp, Centre for Health Economics, University of York.
    8. Meng‐Chi Tang, 2023. "A structural analysis of physician agency and pharmaceutical demand," Health Economics, John Wiley & Sons, Ltd., vol. 32(7), pages 1453-1477, July.
    9. Stacherl, Barbara & Renner, Anna-Theresa & Weber, Daniela, 2023. "Financial incentives and antibiotic prescribing patterns: Evidence from dispensing physicians in a public healthcare system," Social Science & Medicine, Elsevier, vol. 321(C).
    10. Peter Zweifel, 2022. "Preference measurement in health using experiments," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(1), pages 49-66, March.

Articles

  1. Graham Elliott & Nikolay Kudrin & Kaspar Wüthrich, 2022. "Detecting p‐Hacking," Econometrica, Econometric Society, vol. 90(2), pages 887-906, March.
    See citations under working paper version above.
  2. 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.
  3. Hiroaki Kaido & Kaspar Wüthrich, 2021. "Decentralization estimators for instrumental variable quantile regression models," Quantitative Economics, Econometric Society, vol. 12(2), pages 443-475, May.
    See citations under working paper version above.
  4. Kaspar Wüthrich, 2020. "A Comparison of Two Quantile Models With Endogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 443-456, April.
    See citations under working paper version above.
  5. 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.
  6. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    See citations under working paper version above.
  7. Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," Journal of Econometrics, Elsevier, vol. 210(2), pages 219-235.
    See citations under working paper version above.
  8. Daniel Burkhard & Christian P. R. Schmid & Kaspar Wüthrich, 2019. "Financial incentives and physician prescription behavior: Evidence from dispensing regulations," Health Economics, John Wiley & Sons, Ltd., vol. 28(9), pages 1114-1129, September.
    See citations under working paper version above.
  9. Boes, Stefan & Nüesch, Stephan & Wüthrich, Kaspar, 2015. "Hedonic valuation of the perceived risks of nuclear power plants," Economics Letters, Elsevier, vol. 133(C), pages 109-111.

    Cited by:

    1. Tanaka, Shinsuke & Zabel, Jeffrey, 2018. "Valuing nuclear energy risk: Evidence from the impact of the Fukushima crisis on U.S. house prices," Journal of Environmental Economics and Management, Elsevier, vol. 88(C), pages 411-426.
    2. Zhu, Hongjia & Deng, Yongheng & Zhu, Rong & He, Xiaobo, 2016. "Fear of nuclear power? Evidence from Fukushima nuclear accident and land markets in China," Regional Science and Urban Economics, Elsevier, vol. 60(C), pages 139-154.
    3. Ju-Hee Kim & Young-Kuk Kim & Seung-Hoon Yoo, 2023. "Does Proximity to a Power Plant Affect Housing Property Values of a City in South Korea? An Empirical Investigation," Energies, MDPI, vol. 16(4), pages 1-14, February.
    4. Ando, Michihito & Dahlberg, Matz & Engström, Gustav, 2017. "The Risks of Nuclear Disaster and Its Impact on Housing Prices," Working Paper Series 2017:2, Uppsala University, Department of Economics.
    5. Tajima, Kayo & Yamamoto, Masashi & Ichinose, Daisuke, 2016. "How do agricultural markets respond to radiation risk? Evidence from the 2011 disaster in Japan," Regional Science and Urban Economics, Elsevier, vol. 60(C), pages 20-30.
    6. Hsiao, Cody Yu-Ling & Chen, Hsing Hung, 2018. "The contagious effects on economic development after resuming construction policy for nuclear power plants in Coastal China," Energy, Elsevier, vol. 152(C), pages 291-302.
    7. Kim, Ju-Hee & Lim, Seul-Ye & Yoo, Seung-Hoon, 2023. "Does district heating affect residential property prices? Case study of an urban area in South Korea," Utilities Policy, Elsevier, vol. 83(C).
    8. Wen‐Chi Liao & Yilan Luo & Yajie Sun, 2022. "Information shock of disaster and hazard: Impact of Kaohsiung gas explosions and risk disclosure on the equalizing difference in the housing market," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(6), pages 1492-1531, November.
    9. Chuanwang Sun & Xiaochun Meng & Shuijun Peng, 2017. "Effects of Waste-to-Energy Plants on China’s Urbanization: Evidence from a Hedonic Price Analysis in Shenzhen," Sustainability, MDPI, vol. 9(3), pages 1-18, March.
    10. Fekrazad, Amir, 2019. "Earthquake-risk salience and housing prices: Evidence from California," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 78(C), pages 104-113.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.