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Inverse Probability Tilting for Moment Condition Models with Missing Data

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

We propose a new inverse probability weighting (IPW) estimator for moment condition models with missing data. Our estimator is easy to implement and compares favourably with existing IPW estimators, including augmented IPW 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 data set, the early childhood achievement measure, the main regressor of interest, is missing for many units. Copyright , Oxford University Press.

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File URL: http://hdl.handle.net/10.1093/restud/rdr047
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Article provided by Oxford University Press in its journal The Review of Economic Studies.

Volume (Year): 79 (2012)
Issue (Month): 3 ()
Pages: 1053-1079

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Handle: RePEc:oup:restud:v:79:y:2012:i:3:p:1053-1079
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  1. Wooldridge, Jeffrey M., 2001. "Asymptotic Properties Of Weighted M-Estimators For Standard Stratified Samples," Econometric Theory, Cambridge University Press, vol. 17(02), pages 451-470, April.
  2. Imbens, G.W. & Lancaster, T., 1991. "Efficient estimation and stratified sampling," Discussion Paper 1991-45, Tilburg University, Center for Economic Research.
  3. Guido W. Imbens & Whitney Newey & Geert Ridder, 2006. "Mean-squared-error Calculations for Average Treatment Effects," IEPR Working Papers 06.57, Institute of Economic Policy Research (IEPR).
  4. M Arellano & Costas Megir & Mary Silles, 1990. "Female Labour Supply and On-the-Job Search: An Empirical Model Estimated using Complementary Data Sets," CEP Discussion Papers dp0009, Centre for Economic Performance, LSE.
  5. Keisuke Hirano & Guido W. Imbens & Geert Ridder & Donald B. Rebin, 1998. "Combining Panel Data Sets with Attrition and Refreshment Samples," NBER Technical Working Papers 0230, National Bureau of Economic Research, Inc.
  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. 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. Nevo, Aviv, 2003. "Using Weights to Adjust for Sample Selection When Auxiliary Information Is Available," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 43-52, January.
  9. Qian, Hailong & Schmidt, Peter, 1999. "Improved instrumental variables and generalized method of moments estimators," Journal of Econometrics, Elsevier, vol. 91(1), pages 145-169, July.
  10. J. Currie & A. Yelowitz, . "Are Public Housing Projects Good For Kids?," Institute for Research on Poverty Discussion Papers 1152-97, University of Wisconsin Institute for Research on Poverty.
  11. Tripathi, Gautam, 2011. "Moment-Based Inference With Stratified Data," Econometric Theory, Cambridge University Press, vol. 27(01), pages 47-73, February.
  12. Imbens, G.W. & Lancaster, T., 1993. "Case-Control Studies with Contaminated Controls," Discussion Paper 1993-7, Tilburg University, Center for Economic Research.
  13. Imbens, G.W. & Johnson, P. & Spady, R.H., 1995. "Information Theoretic Approaches to Inference in Movement Condition Models," Economics Papers 99, Economics Group, Nuffield College, University of Oxford.
  14. Guido W Imbens, Phillip Johnson & Richard H Spady, . "Information theoretic approaches to inference in moment condition model," Economics Papers W12., Economics Group, Nuffield College, University of Oxford.
  15. Newey, Whitney K, 1990. "Semiparametric Efficiency Bounds," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 5(2), pages 99-135, April-Jun.
  16. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
  17. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
  18. Joshua D. Angrist & Alan B. Krueger, 1990. "The Effect of Age at School Entry on Educational Attainment: An Application of Instrumental Variables with Moments from Two Samples," NBER Working Papers 3571, National Bureau of Economic Research, Inc.
  19. Cosslett, Stephen R, 1981. "Maximum Likelihood Estimator for Choice-Based Samples," Econometrica, Econometric Society, vol. 49(5), pages 1289-1316, September.
  20. Whitney Newey & Richard Smith, 2003. "Higher order properties of GMM and generalised empirical likelihood estimators," CeMMAP working papers CWP04/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  21. repec:cep:stiecm:/2003/451 is not listed on IDEAS
  22. Xiaohong Chen & Han Hong & Alessandro Tarozzi, 2008. "Semiparametric Efficiency in GMM Models of Nonclassical Measurement Errors, Missing Data and Treatment Effects," Cowles Foundation Discussion Papers 1644, Cowles Foundation for Research in Economics, Yale University.
  23. 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.
  24. Wang Q. & Linton O. & Hardle W., 2004. "Semiparametric Regression Analysis With Missing Response at Random," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 334-345, January.
  25. Xiaohong Chen & Han Hong & Elie Tamer, 2005. "Measurement Error Models with Auxiliary Data," Review of Economic Studies, Oxford University Press, vol. 72(2), pages 343-366.
  26. 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.
  27. Wooldridge, Jeffrey M., 2007. "Inverse probability weighted estimation for general missing data problems," Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
  28. Imbens, G.W. & Lancaster, T., 1991. "Combining Micro and Macro Data in Microeconometric Models," Harvard Institute of Economic Research Working Papers 1578, Harvard - Institute of Economic Research.
  29. 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.
  30. Tarozzi, Alessandro, 2007. "Calculating Comparable Statistics From Incomparable Surveys, With an Application to Poverty in India," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 314-336, July.
  31. Nevo, Aviv, 2002. "Sample selection and information-theoretic alternatives to GMM," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 149-157, March.
  32. 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.
  33. 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.
  34. Ridder, Geert & Moffitt, Robert, 2007. "The Econometrics of Data Combination," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 75 Elsevier.
  35. Jeffrey M. Wooldridge, 1999. "Asymptotic Properties of Weighted M-Estimators for Variable Probability Samples," Econometrica, Econometric Society, vol. 67(6), pages 1385-1406, November.
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