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A wild bootstrap algorithm for propensity score matching estimators

Listed author(s):
  • Huber, Martin
  • Camponovo, Lorenzo
  • Bodory, Hugo
  • Lechner, Michael

We introduce a wild bootstrap algorithm for the approximation of the sampling distribution of pair or one-to-many propensity score matching estimators. Unlike the conventional iid bootstrap, the proposed wild bootstrap approach does not construct bootstrap samples by randomly resampling from the observations with uniform weights. Instead, it fixes the covariates and constructs the bootstrap approximation by perturbing the martingale representation for matching estimators. We also conduct a simulation study in which the suggested wild bootstrap performs well even when the sample size is relatively small. Finally, we provide an empirical illustration by analyzing an information intervention in rural development programs.

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Paper provided by Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland in its series FSES Working Papers with number 470.

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Length: 15 pages
Date of creation: 07 Jul 2016
Handle: RePEc:fri:fribow:fribow00470
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  1. Alberto Abadie & Guido W. Imbens, 2016. "Matching on the Estimated Propensity Score," Econometrica, Econometric Society, vol. 84, pages 781-807, 03.
  2. Bodory, Hugo & Camponovo, Lorenzo & Huber, Martin & Lechner, Michael, 2016. "The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators," IZA Discussion Papers 9706, Institute for the Study of Labor (IZA).
  3. Donald W. K. Andrews, 2002. "Higher-Order Improvements of a Computationally Attractive "k"-Step Bootstrap for Extremum Estimators," Econometrica, Econometric Society, vol. 70(1), pages 119-162, January.
  4. Alberto Abadie & Guido W. Imbens, 2012. "A Martingale Representation for Matching Estimators," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 833-843, June.
  5. Abadie, Alberto & Imbens, Guido W., 2011. "Bias-Corrected Matching Estimators for Average Treatment Effects," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 1-11.
  6. Alberto Abadie & Guido W. Imbens, 2008. "On the Failure of the Bootstrap for Matching Estimators," Econometrica, Econometric Society, vol. 76(6), pages 1537-1557, November.
  7. Davidson, Russell & MacKinnon, James G, 1999. "Bootstrap Testing in Nonlinear Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 487-508, May.
  8. Taisuke Otsu & Yoshiyasu Rai, 2015. "Bootstrap inference of matching estimators for average treatment effects," STICERD - Econometrics Paper Series /2015/580, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  9. Huber, Martin & Kotevska, Ana & Martinovska-Stojcheska, A. & Solovyeva, Anna, 2016. "Evaluating an information campaign about rural development policies in (FYR) Macedonia," FSES Working Papers 469, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
  10. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," Review of Economic Studies, Oxford University Press, vol. 65(2), pages 261-294.
  11. 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.
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