The performance of estimators based on the propensity score

Item Type Journal paper
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 observed 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 are important for all estimators. A 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 and different types of outcome variables is considered an important property.

Authors Huber, Martin; Lechner, Michael & Wunsch, Conny
Journal or Publication Title Journal of Econometrics
Language English
Keywords Propensity score matching, kernel matching, inverse probability weighting, inverse probability tilting, selection on observables, empirical Monte Carlo study, finite sample properties.
Subjects economics
HSG Classification contribution to scientific community
HSG Profile Area SEPS - Quantitative Economic Methods
Refereed Yes
Date 27 February 2013
Publisher Elsevier
Place of Publication Amsterdam
Volume 175
Number 1
Page Range 1-21
Number of Pages 21
ISSN 0304-4076
ISSN-Digital 1872-6895
Publisher DOI 10.1016/j.jeconom.2012.11.006
Depositing User Prof. Ph.D Martin Huber
Date Deposited 09 Jul 2010 11:20
Last Modified 23 Aug 2016 11:08
URI: https://www.alexandria.unisg.ch/publications/68111

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Citation

Huber, Martin; Lechner, Michael & Wunsch, Conny (2013) The performance of estimators based on the propensity score. Journal of Econometrics, 175 (1). 1-21. ISSN 0304-4076

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https://www.alexandria.unisg.ch/id/eprint/68111
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