Empirical Economics

, Volume 31, Issue 2, pp 333–367

Semiparametric estimation of conditional mean functions with missing data

Combining parametric moments with matching
Original Paper

DOI: 10.1007/s00181-005-0019-4

Cite this article as:
Frölich, M. Empirical Economics (2006) 31: 333. doi:10.1007/s00181-005-0019-4

Abstract

A new semiparametric estimator for estimating conditional expectation functions from incomplete data is proposed, which integrates parametric regression with nonparametric matching estimators. Besides its applicability to missing data situations due to non-response or attrition, the estimator can also be used for analyzing treatment effect heterogeneity and statistical treatment rules, where data on potential outcomes is missing by definition. By combining moments from a parametric specification with nonparametric estimates of mean outcomes in the non-responding population within a GMM framework, the estimator seeks to balance a good fit in the responding population with low bias in the non-responding population. The estimator is applied to analyzing treatment effect heterogeneity among Swedish rehabilitation programmes.

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.Department of EconomicsUniversity College LondonLondonUK
  2. 2.University of St.Gallen, SIAWSt.GallenSwitzerland

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