Semiparametric estimation of conditional mean functions with missing data
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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.
KeywordsMean Square Error Propensity Score Propensity Score Match Vocational Rehabilitation Average Bias
The author is also affiliated with the Institute for the Study of Labor (IZA), Bonn. I am grateful for discussions and comments to Bo Honoré, Francois Laisney, Michael Lechner, Ruth Miquel, Oivind Nilsen, Jeff Smith, the editor and three anonymous referees. This research was supported by the Swiss National Science Foundation (project NSF 4043-058311) and the Grundlagenforschungsfonds HSG (project G02110112).
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