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Testing for covariate balance using quantile regression and resampling methods

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  • Martin Huber

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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 nontreated units. Conventional balance tests merely check for differences in covariates' means, but cannot account for differences in higher moments. Specification tests constitute an alternative, but might reject misspecified, but yet balancing propensity score models. This paper proposes balance tests based on (i) quantile regression to check for differences in the distributions of continuous covariates and (ii) resampling methods to estimate the distributions of the proposed Kolmogorov-Smirnov and Cramer-von-Mises-Smirnov test statistics. Simulations suggest that the tests capture imbalances related to higher moments when conventional balance tests fail to do so and correctly keep misspecified, but balancing propensity scores when specification tests reject the null.

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Bibliographic Info

Paper provided by Department of Economics, University of St. Gallen in its series University of St. Gallen Department of Economics working paper series 2010 with number 2010-18.

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Length: 34 pages
Date of creation: Jun 2010
Date of revision:
Handle: RePEc:usg:dp2010:2010-18

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Keywords: Balancing property; balance test; propensity score matching;

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References

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  1. Abadie A., 2002. "Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 284-292, March.
  2. Zhao, Zhong, 2005. "Sensitivity of Propensity Score Methods to the Specifications," IZA Discussion Papers 1873, Institute for the Study of Labor (IZA).
  3. Lawrence M. Berger & Jennifer Hill & Jane Waldfogel, 2005. "Maternity leave, early maternal employment and child health and development in the US," Economic Journal, Royal Economic Society, vol. 115(501), pages F29-F47, 02.
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  5. Guido Imbens, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometric Society World Congress 2000 Contributed Papers 1166, Econometric Society.
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  7. Shaikh, Azeem M. & Simonsen, Marianne & Vytlacil, Edward J. & Yildiz, Nese, 2009. "A specification test for the propensity score using its distribution conditional on participation," Journal of Econometrics, Elsevier, vol. 151(1), pages 33-46, July.
  8. Petri Böckerman & Pekka Ilmakunnas, 2009. "Unemployment and self-assessed health: evidence from panel data," Health Economics, John Wiley & Sons, Ltd., vol. 18(2), pages 161-179.
  9. Ichino, Andrea & Mealli, Fabrizia & Nannicini, Tommaso, 2006. "From Temporary Help Jobs to Permanent Employment: What Can We Learn from Matching Estimators and their Sensitivity?," IZA Discussion Papers 2149, Institute for the Study of Labor (IZA).
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  14. Heckman, James J & Ichimura, Hidehiko & Todd, Petra E, 1997. "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 605-54, October.
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Cited by:
  1. 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.
  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, 2010. "How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score," IZA Discussion Papers 5268, Institute for the Study of Labor (IZA).

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