Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection
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- Arun Advani & Toru Kitagawa & Tymon Słoczyński, 2019. "Mostly harmless simulations? Using Monte Carlo studies for estimator selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 893-910, September.
- Advani, Arun & Kitagawa, Toru & Słoczyński, Tymon, 2019. "Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection," The Warwick Economics Research Paper Series (TWERPS) 1192, University of Warwick, Department of Economics.
- Arun Advani & Toru Kitagawa & Tymon S{l}oczy'nski, 2018. "Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection," Papers 1809.09527, arXiv.org, revised Apr 2019.
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Citations
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Cited by:
- Lechner, Michael, 2018.
"Modified Causal Forests for Estimating Heterogeneous Causal Effects,"
IZA Discussion Papers
12040, Institute of Labor Economics (IZA).
- Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," CEPR Discussion Papers 13430, C.E.P.R. Discussion Papers.
- Michael Lechner, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," Papers 1812.09487, arXiv.org, revised Jul 2019.
- Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," Economics Working Paper Series 1901, University of St. Gallen, School of Economics and Political Science.
- 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.
- Susan Athey & Guido W. Imbens & Jonas Metzger & Evan M. Munro, 2019. "Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations," NBER Working Papers 26566, National Bureau of Economic Research, Inc.
- Arun Advani & Toru Kitagawa & Tymon Słoczyński, 2019.
"Mostly harmless simulations? Using Monte Carlo studies for estimator selection,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 893-910, September.
- Arun Advani & Toru Kitagawa & Tymon S{l}oczy'nski, 2018. "Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection," Papers 1809.09527, arXiv.org, revised Apr 2019.
- Advani, Arun & Kitagawa, Toru & Słoczyński, Tymon, 2019. "Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection," The Warwick Economics Research Paper Series (TWERPS) 1192, University of Warwick, Department of Economics.
- Advani, Arun & Kitagawa, Toru & Sloczynski, Tymon, 2019. "Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection," CAGE Online Working Paper Series 411, Competitive Advantage in the Global Economy (CAGE).
- Florian Gunsilius, 2019. "A path-sampling method to partially identify causal effects in instrumental variable models," Papers 1910.09502, arXiv.org, revised Jun 2020.
More about this item
Keywords
empirical Monte Carlo studies; program evaluation; selection on observables; treatment effects JEL Classification: C15; C21; C25; C52;JEL classification:
- C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
- J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
- J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2019-04-22 (Econometrics)
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