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Matching using semiparametric propensity scores

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  • Steven Lehrer

    ()

  • Gregory Kordas

Abstract

This paper considers the application of semiparametric methods to estimate propensity scores or probabilities of program participation, which are central to certain program evaluation methods. To evaluate the practical benefits, we first conduct a Monte Carlo study. Second, we use data from the NSW experiment, CPS, and PSID. We compare treatment effect and evaluation bias estimates using propensity scores estimated from parametric logit, semiparametric single index, and semiparametric binary quantile regression models. Our results suggest that it is important to account for very general forms of heterogeneity in (semiparametric) estimation of the propensity score, particularly when the treatment effects vary in an unsystematic manner with the true propensity score. Copyright Springer-Verlag 2013

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Bibliographic Info

Article provided by Springer in its journal Empirical Economics.

Volume (Year): 44 (2013)
Issue (Month): 1 (February)
Pages: 13-45

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Handle: RePEc:spr:empeco:v:44:y:2013:i:1:p:13-45

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Related research

Keywords: Propensity score matching; Treatment effects; Semiparametric binary choice estimators; Heterogeneity; Binary quantile regression; C14; C81; C99; H53; I38;

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References

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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 604-20, September.
  2. 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.
  3. Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-31, May.
  4. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
  5. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
  6. Busso, Matias & DiNardo, John & McCrary, Justin, 2009. "New Evidence on the Finite Sample Properties of Propensity Score Matching and Reweighting Estimators," IZA Discussion Papers 3998, Institute for the Study of Labor (IZA).
  7. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
  8. 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.
  9. Heckman, James J. & Todd, Petra E., 2009. "A Note on Adapting Propensity Score Matching and Selection Models to Choice Based Samples," IZA Discussion Papers 4304, Institute for the Study of Labor (IZA).
  10. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  11. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, 07.
  12. Rajeev H. Dehejia & Sadek Wahba, 1998. "Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs," NBER Working Papers 6586, National Bureau of Economic Research, Inc.
  13. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
  14. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," NBER Working Papers 6699, National Bureau of Economic Research, Inc.
  15. Hotz, V. Joseph & Crump, Richard K. & Mitnik, Oscar A. & Imbens, Guido, 2009. "Dealing with Limited Overlap in Estimation of Average Treatment Effects," Scholarly Articles 3007645, Harvard University Department of Economics.
  16. Daniel Millimet & Rusty Tchernis, 2008. "On the Specification of Propensity Scores: with Applications to the Analysis of Trade Policies," Caepr Working Papers 2006-013_Updated, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
  17. Shakeeb Khan & Elie Tamer, 2010. "Irregular Identification, Support Conditions, and Inverse Weight Estimation," Econometrica, Econometric Society, vol. 78(6), pages 2021-2042, November.
  18. Ichimura, H., 1991. "Semiparametric Least Squares (sls) and Weighted SLS Estimation of Single- Index Models," Papers 264, Minnesota - Center for Economic Research.
  19. Heckman, James J & Ichimura, Hidehiko & Todd, Petra, 1998. "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, Wiley Blackwell, vol. 65(2), pages 261-94, April.
  20. Galdo, Jose C. & Smith, Jeffrey A. & Black, Dan A., 2007. "Bandwidth Selection and the Estimation of Treatment Effects with Unbalanced Data," IZA Discussion Papers 3095, Institute for the Study of Labor (IZA).
  21. Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
  22. 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.
  23. Dehejia, Rajeev, 2005. "Practical propensity score matching: a reply to Smith and Todd," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 355-364.
  24. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
  25. 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.
  26. 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.
  27. Lechner, Michael, 1991. "Testing Logit Models in Practice," Empirical Economics, Springer, vol. 16(2), pages 177-98.
  28. 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.
  29. Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
  30. 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.
  31. Zhao, Zhong, 2008. "Sensitivity of propensity score methods to the specifications," Economics Letters, Elsevier, vol. 98(3), pages 309-319, March.
  32. 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.
  33. Manski, Charles F. & Thompson, T. Scott, 1986. "Operational characteristics of maximum score estimation," Journal of Econometrics, Elsevier, vol. 32(1), pages 85-108, June.
  34. Alberto Abadie & Guido W. Imbens, 2002. "Simple and Bias-Corrected Matching Estimators for Average Treatment Effects," NBER Technical Working Papers 0283, National Bureau of Economic Research, Inc.
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Citations

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Cited by:
  1. Bernd Fitzenberger & Michael Lechner & Jeffrey Smith, 2013. "Estimation of treatment effects: recent developments and applications," Empirical Economics, Springer, vol. 44(1), pages 1-11, February.
  2. Apps, Patricia & Mendolia, Silvia & Walker, Ian, 2012. "The Impact of Pre-school on Adolescents' Outcomes: Evidence from a Recent English Cohort," IZA Discussion Papers 6971, Institute for the Study of Labor (IZA).
  3. Miana Plesca & Jeffrey Smith, 2007. "Evaluating multi-treatment programs: theory and evidence from the U.S. Job Training Partnership Act experiment," Empirical Economics, Springer, vol. 32(2), pages 491-528, May.
  4. Galdo, Jose C. & Smith, Jeffrey A. & Black, Dan A., 2007. "Bandwidth Selection and the Estimation of Treatment Effects with Unbalanced Data," IZA Discussion Papers 3095, Institute for the Study of Labor (IZA).

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