Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/46121 
Year of Publication: 
2010
Series/Report no.: 
IZA Discussion Papers No. 5268
Publisher: 
Institute for the Study of Labor (IZA), Bonn
Abstract: 
We investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observable covariates is required, like inverse probability weighting, kernel and other variants of matching, as well as different parametric models. The simulation design used is based on real data usually employed for the evaluation of labour market programmes in Germany. We vary several dimensions of the design that are of practical importance, like sample size, the type of the outcome variable, and aspects of the selection process. We find that trimming individual observations with too much weight as well as the choice of tuning parameters is important for all estimators. The key conclusion from our simulations is that a particular radius matching estimator combined with regression performs best overall, in particular when robustness to misspecifications of the propensity score is considered an important property.
Subjects: 
propensity score matching
kernel matching
inverse probability weighting
selection on observables
empirical Monte Carlo study
finite sample properties
JEL: 
C21
Document Type: 
Working Paper

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