Practical Procedures to Deal with Common Support Problems in Matching Estimation

Michael Lechner and Anthony Strittmatter

No 10532, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: This paper assesses the performance of common estimators adjusting for differences in covariates, such as matching and regression, when faced with so-called common support problems. It also shows how different procedures suggested in the literature affect the properties of such estimators. Based on an Empirical Monte Carlo simulation design, a lack of common support is found to increase the root mean squared error (RMSE) of all investigated parametric and semiparametric estimators. Dropping observations that are off support usually improves their performance, although the magnitude of the improvement depends on the particular method used.

Keywords: common support; regression; matching estimation; Empirical Monte Carlo Study; outlier; small sample performance (search for similar items in EconPapers)
JEL-codes: C21 J68 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2017-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (37)

Published - published in: Econometric Reviews, 2019, 38 (2), 193-207

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Related works:
Journal Article: Practical procedures to deal with common support problems in matching estimation (2019)
Working Paper: Practical Procedures to Deal with Common Support Problems in Matching Estimation (2014)
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