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Mostly Harmless Simulations? On the Internal Validity of Empirical Monte Carlo Studies

  • Advani, Arun


    (Institute for Fiscal Studies, London)

  • Sloczynski, Tymon


    (Brandeis University)

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.

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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 7874.

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Length: 35 pages
Date of creation: Dec 2013
Date of revision:
Handle: RePEc:iza:izadps:dp7874
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  1. Wooldridge, Jeffrey M. & Imbens, Guido, 2009. "Recent Developments in the Econometrics of Program Evaluation," Scholarly Articles 3043416, Harvard University Department of Economics.
  2. 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.
  3. Blundell, Richard & Costa Dias, Monica, 2008. "Alternative Approaches to Evaluation in Empirical Microeconomics," IZA Discussion Papers 3800, Institute for the Study of Labor (IZA).
  4. 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.
  5. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2002. "How Much Should We Trust Differences-in-Differences Estimates?," NBER Working Papers 8841, National Bureau of Economic Research, Inc.
  6. Brewer, Mike & Crossley, Thomas F. & Joyce, Robert, 2013. "Inference with Difference-in-Differences Revisited," IZA Discussion Papers 7742, Institute for the Study of Labor (IZA).
  7. Lechner, Michael & Wunsch, Conny, 2011. "Sensitivity of Matching-Based Program Evaluations to the Availability of Control Variables," IZA Discussion Papers 5553, Institute for the Study of Labor (IZA).
  8. 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.
  9. 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.
  10. 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.
  11. Yun, Myeong-Su, 2003. "Decomposing Differences in the First Moment," IZA Discussion Papers 877, Institute for the Study of Labor (IZA).
  12. Markus Frölich, 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.
  13. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-20, September.
  14. 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.
  15. 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.
  16. Patrick Kline, 2011. "Oaxaca-Blinder as a Reweighting Estimator," American Economic Review, American Economic Association, vol. 101(3), pages 532-37, May.
  17. Cameron,A. Colin & Trivedi,Pravin K., 2005. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9780521848053, September.
  18. 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, 01.
  19. 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.
  20. Nicole Fortin & Thomas Lemieux & Sergio Firpo, 2010. "Decomposition Methods in Economics," NBER Working Papers 16045, National Bureau of Economic Research, Inc.
  21. Doug Miller & A. Colin Cameron & Jonah B. Gelbach, 2006. "Bootstrap-Based Improvements for Inference with Clustered Errors," Working Papers 621, University of California, Davis, Department of Economics.
  22. James J. Heckman, 1989. "Choosing Among Alternative Nonexperimental Methods for Estimating the Impact of Social Programs: The Case of Manpower Training," NBER Working Papers 2861, National Bureau of Economic Research, Inc.
  23. 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, 08.
  24. Ben Jann, 2008. "The Blinder–Oaxaca decomposition for linear regression models," Stata Journal, StataCorp LP, vol. 8(4), pages 453-479, December.
  25. Markus Frolich & Blaise Melly, 2010. "Estimation of quantile treatment effects with Stata," Stata Journal, StataCorp LP, vol. 10(3), pages 423-457, September.
  26. Alberto Abadie & David Drukker & Jane Leber Herr & Guido W. Imbens, 2004. "Implementing matching estimators for average treatment effects in Stata," Stata Journal, StataCorp LP, vol. 4(3), pages 290-311, September.
  27. 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.
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