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Practical Procedures to Deal with Common Support Problems in Matching Estimation

Listed author(s):
  • Lechner, Michael

    ()

    (University of St. Gallen)

  • Strittmatter, Anthony

    ()

    (University of St. Gallen)

This paper assesses the performance of common estimators adjusting for differences in covariates, such as matching and regression, when faced with so-called common support problems. It also shows how different procedures suggested in the literature affect the properties of such estimators. Based on an Empirical Monte Carlo simulation design, a lack of common support is found to increase the root mean squared error (RMSE) of all investigated parametric and semiparametric estimators. Dropping observations that are off support usually improves their performance, although the magnitude of the improvement depends on the particular method used.

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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 10532.

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Length: 29 pages
Date of creation: Jan 2017
Handle: RePEc:iza:izadps:dp10532
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  1. Doerr, Annabelle & Fitzenberger, Bernd & Kruppe, Thomas & Paul, Marie & Strittmatter, Anthony, 2014. "Employment and earnings effects of awarding training vouchers in Germany," ZEW Discussion Papers 14-065, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  2. 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.
  3. David Card & Jochen Kluve & Andrea Weber, 2010. "Active Labour Market Policy Evaluations: A Meta-Analysis," Economic Journal, Royal Economic Society, vol. 120(548), pages 452-477, November.
  4. Jeffrey Smith & Petra Todd, 2003. "Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators?," University of Western Ontario, Centre for Human Capital and Productivity (CHCP) Working Papers 20035, University of Western Ontario, Centre for Human Capital and Productivity (CHCP).
  5. Ulf Rinne & Arne Uhlendorff & Zhong Zhao, 2013. "Vouchers and caseworkers in training programs for the unemployed," Empirical Economics, Springer, vol. 45(3), pages 1089-1127, December.
  6. Guido Imbens, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometric Society World Congress 2000 Contributed Papers 1166, Econometric Society.
  7. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
  8. Guido Imbens & Thomas Lemieux, 2007. "Regression Discontinuity Designs: A Guide to Practice," NBER Working Papers 13039, National Bureau of Economic Research, Inc.
  9. Lechner, Michael & Wunsch, Conny, 2011. "Sensitivity of matching-based program evaluations to the availability of control variables," CEPR Discussion Papers 8294, C.E.P.R. Discussion Papers.
  10. Martin Biewen & Bernd Fitzenberger & Aderonke Osikominu & Marie Paul, 2012. "The Effectiveness of Public Sponsored Training Revisited: The Importance of Data and Methodological Choices," NRN working papers 2012-09, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
  11. Shakeeb Khan & Elie Tamer, 2010. "Irregular Identification, Support Conditions, and Inverse Weight Estimation," Econometrica, Econometric Society, vol. 78(6), pages 2021-2042, November.
  12. Imbens, Guido W. & Kalyanaraman, Karthik, 2009. "Optimal Bandwidth Choice for the Regression Discontinuity Estimator," IZA Discussion Papers 3995, Institute for the Study of Labor (IZA).
  13. Guido W. Imbens, 2003. "Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review," NBER Technical Working Papers 0294, National Bureau of Economic Research, Inc.
  14. Huber, Martin & Lechner, Michael & Steinmayr, Andreas, 2012. "Radius matching on the propensity score with bias adjustment: finite sample behaviour, tuning parameters and software implementation," Economics Working Paper Series 1226, University of St. Gallen, School of Economics and Political Science.
  15. 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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