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Efficiency bounds for missing data models with semiparametric restrictions

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  • Bryan S. Graham

Abstract

This paper shows that the semiparametric efficiency bound for a parameter identified by an unconditional moment restriction with data missing at random (MAR) coincides with that of a particular augmented moment condition problem. The augmented system consists of the inverse probability weighted (IPW) original moment restriction and an additional conditional moment restriction which exhausts all other implications of the MAR assumption. The paper also investigates the value of additional semiparametric restrictions on the conditional expectation function (CEF) of the original moment function given always- observed covariates. In the program evaluation context, for example, such restrictions are implied by semiparametric models for the potential outcome CEFs given baseline covariates. The efficiency bound associated with this model is shown to also coincide with that of a particular moment condition problem. Some implications of these results for estimation are briefly discussed.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 14376.

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Date of creation: Oct 2008
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Handle: RePEc:nbr:nberwo:14376

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Cited by:
  1. Thomas MaCurdy & Xiaohong Chen & Han Hong, 2011. "Flexible Estimation of Treatment Effect Parameters," American Economic Review, American Economic Association, vol. 101(3), pages 544-51, May.
  2. 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.

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