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Testing for covariate balance using quantile regression and resampling methods

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  • Martin Huber

Abstract

Consistency of propensity score matching estimators hinges on the propensity score's ability to balance the distributions of covariates in the pools of treated and non-treated units. Conventional balance tests merely check for differences in covariates’ means, but cannot account for differences in higher moments. For this reason, this paper proposes balance tests which test for differences in the entire distributions of continuous covariates based on quantile regression (to derive Kolmogorov--Smirnov and Cramer--von-Mises--Smirnov-type test statistics) and resampling methods (for inference). Simulations suggest that these methods are very powerful and capture imbalances related to higher moments when conventional balance tests fail to do so.

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File URL: http://hdl.handle.net/10.1080/02664763.2011.570323
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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Journal of Applied Statistics.

Volume (Year): 38 (2011)
Issue (Month): 12 (February)
Pages: 2881-2899

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Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2881-2899

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Cited by:
  1. Martin Huber & Michael Lechner & Conny Wunsch, 2010. "How to control for many covariates? Reliable estimators based on the propensity score," University of St. Gallen Department of Economics working paper series 2010 2010-30, Department of Economics, University of St. Gallen.
  2. Dehejia, Rajeev, 2013. "The porous dialectic: Experimental and non-experimental methods in development economics," Working Paper Series UNU-WIDER Research Paper , World Institute for Development Economic Research (UNU-WIDER).
  3. 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.

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