Semiparametric estimation of conditional mean functions with missing data - combining parametric moments with matching
Type
discussion paper
Date Issued
2004-01-01
Author(s)
Froelich, Markus
Abstract
revised version of Discussion paper 2001-16#### 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. Download Discussion Paper: (pdf, 562 kb) Download Appendix: (pdf, 354 kb) former title: Treatment Choice based on semiparametric evaluation methods
Funding(s)
Language
English
HSG Classification
contribution to scientific community
Refereed
No
Subject(s)
Division(s)
Eprints ID
15839
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open.access
Name
GMM_appendix.pdf
Size
353.4 KB
Format
Adobe PDF
Checksum (MD5)
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Name
GMM_revised.pdf
Size
561.39 KB
Format
Adobe PDF
Checksum (MD5)
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