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Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST)

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  • Bryan S. Graham
  • Cristine Campos de Xavier Pinto
  • Daniel Egel

Abstract

We propose a locally efficient, doubly robust, estimator for a class of semiparametric data combination problems. A leading estimand in this class is the average treatment effect on the treated (ATT). Data combination problems are related to, but distinct from, the class of missing data problems analyzed by Robins, Rotnitzky and Zhao (1994) (of which the Average Treatment Effect (ATE) estimand is a special case). Our procedure may be used to efficiently estimate, among other objects, the ATT, the two-sample instrumental variables model (TSIV), counterfactual distributions, and poverty maps. In an empirical application we use our procedure to characterize residual Black-White wage inequality after flexibly controlling for 'pre-market' differences in measured cognitive achievement as in Neal and Johnson (1996). We find that residual Black-White inequality is negligible at lower and higher quantiles of the Black wage distribution, but substantial at middle quantiles.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 16928.

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Date of creation: Apr 2011
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Handle: RePEc:nbr:nberwo:16928

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  1. Patrick Kline, 2011. "Oaxaca-Blinder as a Reweighting Estimator," American Economic Review, American Economic Association, vol. 101(3), pages 532-37, May.
  2. Dinardo, J. & Fortin, N.M. & Lemieux, T., 1994. "Labor Market Institutions and the Distribution of Wages, 1973-1992: a Semiparametric Approach," Cahiers de recherche 9406, Universite de Montreal, Departement de sciences economiques.
  3. Janet Currie & Aaron Yelowitz, 1997. "Are Public Housing Projects Good for Kids?," NBER Working Papers 6305, National Bureau of Economic Research, Inc.
  4. Guido Imbens, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometric Society World Congress 2000 Contributed Papers 1166, Econometric Society.
  5. Bryan S. Graham, 2008. "Efficiency bounds for missing data models with semiparametric restrictions," NBER Working Papers 14376, National Bureau of Economic Research, Inc.
  6. Keisuke Hirano & Guido W. Imbens & Geert Ridder & Donald B. Rubin, 1998. "Combining Panel Data Sets with Attrition and Refreshment Samples," Tinbergen Institute Discussion Papers 98-033/4, Tinbergen Institute.
  7. Fortin, Nicole & Lemieux, Thomas & Firpo, Sergio, 2011. "Decomposition Methods in Economics," Handbook of Labor Economics, Elsevier.
  8. Bryan S. Graham & Cristine Campos De Xavier Pinto & Daniel Egel, 2012. "Inverse Probability Tilting for Moment Condition Models with Missing Data," Review of Economic Studies, Oxford University Press, vol. 79(3), pages 1053-1079.
  9. Guido W. Imbens & Judith K. Hellerstein, 1996. "Imposing Moment Restrictions from Auxiliary Data by Weighting," NBER Technical Working Papers 0202, National Bureau of Economic Research, Inc.
  10. Robert Barsky & John Bound & Kerwin Charles & Joseph Lupton, 2001. "Accounting for the Black-White Wealth Gap: A Nonparametric Approach," NBER Working Papers 8466, National Bureau of Economic Research, Inc.
  11. Neal, Derek A & Johnson, William R, 1996. "The Role of Premarket Factors in Black-White Wage Differences," Journal of Political Economy, University of Chicago Press, vol. 104(5), pages 869-95, October.
  12. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
  13. William A. Darity & Patrick L. Mason, 1998. "Evidence on Discrimination in Employment: Codes of Color, Codes of Gender," Journal of Economic Perspectives, American Economic Association, vol. 12(2), pages 63-90, Spring.
  14. Alessandro Tarozzi & Angus Deaton, 2009. "Using Census and Survey Data to Estimate Poverty and Inequality for Small Areas," The Review of Economics and Statistics, MIT Press, vol. 91(4), pages 773-792, November.
  15. 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.
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Cited by:
  1. Toru Kitagawa & Chris Muris, 2013. "Covariate selection and model averaging in semiparametric estimation of treatment effects," CeMMAP working papers CWP61/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  2. 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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