Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/161155
Authors: 
Lechner, Michael
Strittmatter, Anthony
Year of Publication: 
2017
Series/Report no.: 
IZA Discussion Papers 10532
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.
Subjects: 
Empirical Monte Carlo Study
matching estimation
regression
common support
outlier
small sample performance
JEL: 
C21
J68
Document Type: 
Working Paper

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