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Treatment effects and panel data

  • Lechner, Michael

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It is a major achievement of the econometric treatment effect literature to clarify under which conditions causal effects are non-parametrically identified. The first part of this chapter focuses on the static treatment model. In this part, I show how panel data can be used to improve the credibility of matching and instrumental variable estimators. In practice, these gains come mainly from the availability of outcome variables measured prior to treatment. Such outcome variables also foster the use of alternative identification strategies, in particular so-called difference-in-difference estimation. In addition to improving the credibility of static causal models, panel data may allow credibly estimating dynamic causal models, which is the main theme of the second part of this chapter.

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File URL: http://www1.vwa.unisg.ch/RePEc/usg/econwp/EWP-1314.pdf
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Paper provided by University of St. Gallen, School of Economics and Political Science in its series Economics Working Paper Series with number 1314.

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Length: 42 pages
Date of creation: Jun 2013
Date of revision:
Handle: RePEc:usg:econwp:2013:14
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  1. Fröhlich, Markus & Lechner, Michael, 2006. "Exploiting Regional Treatment Intensity for the Evaluation of Labour Market Policies," CEPR Discussion Papers 5728, C.E.P.R. Discussion Papers.
  2. David Card & Jochen Kluve & Andrea Weber, 2010. "Active Labor Market Policy Evaluations: A Meta-Analysis," NBER Working Papers 16173, National Bureau of Economic Research, Inc.
  3. Guido M. Imbens & Jeffrey M. Wooldridge, 2008. "Recent Developments in the Econometrics of Program Evaluation," NBER Working Papers 14251, National Bureau of Economic Research, Inc.
  4. 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.
  5. Michael Lechner & Ruth Miquel, 2010. "Identification of the effects of dynamic treatments by sequential conditional independence assumptions," Empirical Economics, Springer, vol. 39(1), pages 111-137, August.
  6. Richard Blundell & Monica Costa Dias, 2002. "Alternative approaches to evaluation in empirical microeconomics," CeMMAP working papers CWP10/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  7. Joshua Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design is Taking the Con out of Econometrics," NBER Working Papers 15794, National Bureau of Economic Research, Inc.
  8. Lechner, Michael & Miquel, Ruth & Wunsch, Conny, 2004. "Long-Run Effects of Public Sector Sponsored Training in West Germany," IZA Discussion Papers 1443, Institute for the Study of Labor (IZA).
  9. Angus Deaton, 2010. "Instruments, Randomization, and Learning about Development," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 424-55, June.
  10. Lechner, Michael & Wunsch, Conny, 2011. "Sensitivity of Matching-Based Program Evaluations to the Availability of Control Variables," IZA Discussion Papers 5553, Institute for the Study of Labor (IZA).
  11. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," NBER Technical Working Papers 0251, National Bureau of Economic Research, Inc.
  12. 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.
  13. Frolich, Markus, 2007. "Nonparametric IV estimation of local average treatment effects with covariates," Journal of Econometrics, Elsevier, vol. 139(1), pages 35-75, July.
  14. Heckman, James J. & Navarro, Salvador, 2007. "Dynamic discrete choice and dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 136(2), pages 341-396, February.
  15. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
  16. 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.
  17. Abbring, Jaap H., 2003. "Dynamic Econometric Program Evaluation," IZA Discussion Papers 804, Institute for the Study of Labor (IZA).
  18. Sergio Firpo, 2007. "Efficient Semiparametric Estimation of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 75(1), pages 259-276, 01.
  19. Lechner, Michael, 2009. "Long-run labour market and health effects of individual sports activities," Journal of Health Economics, Elsevier, vol. 28(4), pages 839-854, July.
  20. Joshua D. Angrist & Guido W. Imbens, 1995. "Identification and Estimation of Local Average Treatment Effects," NBER Technical Working Papers 0118, National Bureau of Economic Research, Inc.
  21. 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.
  22. Steven Lehrer & Weili Ding, 2004. "Estimating Dynamic Treatment Effects from Project STAR," Econometric Society 2004 North American Summer Meetings 252, Econometric Society.
  23. James J. Heckman, 2010. "Building Bridges between Structural and Program Evaluation Approaches to Evaluating Policy," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 356-98, June.
  24. Susan Athey & Guido Imbens, 2003. "Identification and Inference in Nonlinear Difference-in-Differences Models," Levine's Working Paper Archive 506439000000000079, David K. Levine.
  25. repec:dgr:kubcen:200845 is not listed on IDEAS
  26. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Wiley Blackwell, vol. 58(2), pages 277-97, April.
  27. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097 Elsevier.
  28. Jaap H. Abbring & Gerard J. van den Berg, 2003. "The Nonparametric Identification of Treatment Effects in Duration Models," Econometrica, Econometric Society, vol. 71(5), pages 1491-1517, 09.
  29. 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).
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