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Mostly harmless simulations? On the internal validity of empirical Monte Carlo studies

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Listed:
  • Arun Advani

    () (Institute for Fiscal Studies)

  • Tymon Sloczynski

    (Institute for Fiscal Studies)

Abstract

In this paper we evaluate the premise from the recent literature on Monte Carlo studies that an empirically motivated simulation exercise is informative about the actual ranking of various estimators when applied to a particular problem. We consider two alternative designs and provide an empirical test for both of them. We conclude that a necessary condition for the simulations to be informative about the true ranking is that the treatment effect in simulations must be equal to the (unknown) true effect. This severely limits the usefulness of such procedures, since were the effect known, the procedure would not be necessary.

Suggested Citation

  • Arun Advani & Tymon Sloczynski, 2013. "Mostly harmless simulations? On the internal validity of empirical Monte Carlo studies," CeMMAP working papers CWP64/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:64/13
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    1. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    2. Lechner, Michael & Wunsch, Conny, 2013. "Sensitivity of matching-based program evaluations to the availability of control variables," Labour Economics, Elsevier, vol. 21(C), pages 111-121.
    3. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    4. repec:ags:stataj:159033 is not listed on IDEAS
    5. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    6. repec:ags:stataj:116250 is not listed on IDEAS
    7. repec:spr:portec:v:1:y:2002:i:2:d:10.1007_s10258-002-0010-3 is not listed on IDEAS
    8. Martin Huber & Michael Lechner & Giovanni Mellace, 2016. "The Finite Sample Performance of Estimators for Mediation Analysis Under Sequential Conditional Independence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 139-160, January.
    9. Bodory, Hugo & Camponovo, Lorenzo & Huber, Martin & Lechner, Michael, 2016. "The finite sample performance of inference methods for propensity score matching and weighting estimators," Economics Working Paper Series 1604, University of St. Gallen, School of Economics and Political Science.
    10. Yun, Myeong-Su, 2004. "Decomposing differences in the first moment," Economics Letters, Elsevier, vol. 82(2), pages 275-280, February.
    11. Richard Blundell & Monica Costa Dias, 2009. "Alternative Approaches to Evaluation in Empirical Microeconomics," Journal of Human Resources, University of Wisconsin Press, vol. 44(3).
    12. Michael Lechner & Anthony Strittmatter, 2019. "Practical procedures to deal with common support problems in matching estimation," Econometric Reviews, Taylor & Francis Journals, vol. 38(2), pages 193-207, February.
    13. Markus Frlich, 2004. "Finite-Sample Properties of Propensity-Score Matching and Weighting Estimators," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 77-90, February.
    14. Heckman, J.J. & Hotz, V.J., 1988. "Choosing Among Alternative Nonexperimental Methods For Estimating The Impact Of Social Programs: The Case Of Manpower Training," University of Chicago - Economics Research Center 88-12, Chicago - Economics Research Center.
    15. Matias Busso & John DiNardo & Justin McCrary, 2014. "New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 885-897, December.
    16. Frölich, Markus & Melly, Blaise, 2010. "Estimation of quantile treatment effects with Stata," Stata Journal, StataCorp LP, vol. 10(3), pages 1-35.
    17. Ahmed Khwaja & Gabriel Picone & Martin Salm & Justin G. Trogdon, 2011. "A comparison of treatment effects estimators using a structural model of AMI treatment choices and severity of illness information from hospital charts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 825-853, August.
    18. repec:bpj:jecome:v:7:y:2018:i:1:p:16:n:9 is not listed on IDEAS
    19. Alberto Abadie & Guido W. Imbens, 2011. "Bias-Corrected Matching Estimators for Average Treatment Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 1-11, January.
    20. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    21. Fortin, Nicole & Lemieux, Thomas & Firpo, Sergio, 2011. "Decomposition Methods in Economics," Handbook of Labor Economics, Elsevier.
    22. Zhao, Zhong, 2008. "Sensitivity of propensity score methods to the specifications," Economics Letters, Elsevier, vol. 98(3), pages 309-319, March.
    23. Oaxaca, Ronald, 1973. "Male-Female Wage Differentials in Urban Labor Markets," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(3), pages 693-709, October.
    24. Jann, Ben, 2008. "The Blinder–Oaxaca decomposition for linear regression models," Stata Journal, StataCorp LP, vol. 8(4), pages 1-27.
    25. Abadie, Alberto & Drukker, David M. & Herr, Jane Leber & Imbens, Guido W., 2004. "Implementing matching estimators for average treatment effects in Stata," Stata Journal, StataCorp LP, vol. 4(3), pages 1-22.
    26. Bryan S. Graham & Cristine Campos De Xavier Pinto & Daniel Egel, 2012. "Inverse Probability Tilting for Moment Condition Models with Missing Data," Review of Economic Studies, Oxford University Press, vol. 79(3), pages 1053-1079.
    27. Millimet, Daniel L. & Tchernis, Rusty, 2009. "On the Specification of Propensity Scores, With Applications to the Analysis of Trade Policies," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(3), pages 397-415.
    28. Hansen, Christian B., 2007. "Generalized least squares inference in panel and multilevel models with serial correlation and fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 670-694, October.
    29. Juan Díaz & Tomás Rau & Jorge Rivera, 2015. "A Matching Estimator Based on a Bilevel Optimization Problem," The Review of Economics and Statistics, MIT Press, vol. 97(4), pages 803-812, October.
    30. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, Oxford University Press, vol. 119(1), pages 249-275.
    31. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
    32. Cameron,A. Colin & Trivedi,Pravin K., 2005. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9780521848053.
    33. Alan S. Blinder, 1973. "Wage Discrimination: Reduced Form and Structural Estimates," Journal of Human Resources, University of Wisconsin Press, vol. 8(4), pages 436-455.
    34. Zhong Zhao, 2004. "Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 91-107, February.
    35. Alexis Diamond & Jasjeet S. Sekhon, 2013. "Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 932-945, July.
    36. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, January.
    37. Patrick Kline, 2011. "Oaxaca-Blinder as a Reweighting Estimator," American Economic Review, American Economic Association, vol. 101(3), pages 532-537, May.
    38. Brewer Mike & Crossley Thomas F. & Joyce Robert, 2018. "Inference with Difference-in-Differences Revisited," Journal of Econometric Methods, De Gruyter, vol. 7(1), pages 1-16, January.
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    Cited by:

    1. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
    2. Martin Huber & Michael Lechner & Giovanni Mellace, 2016. "The Finite Sample Performance of Estimators for Mediation Analysis Under Sequential Conditional Independence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 139-160, January.
    3. Tymon Słoczyński, 2015. "The Oaxaca–Blinder Unexplained Component as a Treatment Effects Estimator," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(4), pages 588-604, August.

    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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