Matching As An Econometric Evaluation Estimator

James J. Heckman, Hidehiko Ichimura, Petra Todd

Research output: Contribution to journalArticle

1378 Scopus citations

Abstract

This paper develops the method of matching as an econometric evaluation estimator. A rigorous distribution theory for kernel-based matching is presented. The method of matching is extended to more general conditions than the ones assumed in the statistical literature on the topic. We focus on the method of propensity score matching and show that it is not necessarily better, in the sense of reducing the variance of the resulting estimator, to use the propensity score method even if propensity score is known. We extend the statistical literature on the propensity score by considering the case when it is estimated both parametrically and nonparametrically. We examine the benefits of separability and exclusion restrictions in improving the efficiency of the estimator. Our methods also apply to the econometric selection bias estimator.

Original languageEnglish (US)
Pages (from-to)261-294
Number of pages34
JournalReview of Economic Studies
Volume65
Issue number2
DOIs
StatePublished - Apr 1998

ASJC Scopus subject areas

  • Economics and Econometrics

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