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Inverse Probability Tilting for Moment Condition Models with Missing Data

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

Abstract

We propose a new inverse probability weighting (IPW) estimator for moment condition models with missing data. Our estimator is easy to implement and compares favorably with existing IPW estimators, including augmented inverse probability weighting (AIPW) estimators, in terms of efficiency, robustness, and higher order bias. We illustrate our method with a study of the relationship between early Black-White differences in cognitive achievement and subsequent differences in adult earnings. In our dataset the early childhood achievement measure, the main regressor of interest, is missing for many units.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 13981.

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Date of creation: May 2008
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Publication status: published as 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.
Handle: RePEc:nbr:nberwo:13981

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Citations

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Cited by:
  1. Bryan S. Graham & Cristine Campos de Xavier Pinto & Daniel Egel, 2011. "Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST)," NBER Working Papers 16928, National Bureau of Economic Research, Inc.
  2. Christopher R. Bollinger & Barry T. Hirsch, 2010. "GDP & Beyond – die europäische Perspektive," Working Paper Series of the German Council for Social and Economic Data, German Council for Social and Economic Data (RatSWD) 165, German Council for Social and Economic Data (RatSWD).
  3. Rothe, Christoph & Firpo, Sergio, 2013. "Semiparametric Estimation and Inference Using Doubly Robust Moment Conditions," IZA Discussion Papers 7564, Institute for the Study of Labor (IZA).
  4. Bollinger, Christopher R. & Hirsch, Barry, 2010. "Is Earnings Nonresponse Ignorable?," IZA Discussion Papers 5347, Institute for the Study of Labor (IZA).
  5. Carlos A. Flores & Oscar A. Mitnik, 2013. "Comparing Treatments across Labor Markets: An Assessment of Nonexperimental Multiple-Treatment Strategies," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1691-1707, December.
  6. Sloczynski, Tymon & Wooldridge, Jeffrey M., 2014. "A General Double Robustness Result for Estimating Average Treatment Effects," IZA Discussion Papers 8084, Institute for the Study of Labor (IZA).
  7. Bazzi, Samuel & Sumarto, Sudarno & Suryahadi, Asep, 2013. "It's All in the Timing:Household Expenditure and Labor Supply Responses to Unconditional Cash Transfers," MPRA Paper 57892, University Library of Munich, Germany, revised 31 Nov 2013.
  8. Bryan S. Graham & Keisuke Hirano, 2011. "Robustness to Parametric Assumptions in Missing Data Models," American Economic Review, American Economic Association, American Economic Association, vol. 101(3), pages 538-43, May.
  9. Òscar Jordà & Alan M. Taylor, 2013. "The time for austerity: estimating the average treatment effect of fiscal policy," Working Paper Series, Federal Reserve Bank of San Francisco 2013-25, Federal Reserve Bank of San Francisco.
  10. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, Elsevier, vol. 175(1), pages 1-21.
  11. Toru Kitagawa & Chris Muris, 2013. "Covariate selection and model averaging in semiparametric estimation of treatment effects," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies CWP61/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  12. 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.
  13. Wang-Sheng Lee, 2013. "Propensity score matching and variations on the balancing test," Empirical Economics, Springer, Springer, vol. 44(1), pages 47-80, February.

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