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The finite sample performance of inference methods for propensity score matching and weighting estimators

Bodory, Hugo, Camponovo, Lorenzo, Huber, Martin and Lechner, Michael (2018) The finite sample performance of inference methods for propensity score matching and weighting estimators Journal of Business & Economic Statistics. pp. 1-43.

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Abstract

This paper investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyse both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation designs, which are based on German register data and U.S. survey data. We vary the design w.r.t. treatment selectivity, effect heterogeneity, share of treated, and sample size. The results suggest that in general, theoretically justified bootstrap procedures (i.e. wild bootstrapping for pair matching and standard bootstrapping for ‘smoother’ treatment effect estimators) dominate the asymptotic approximations in terms of coverage rates for both matching and weighting estimators. Most findings are robust across simulation designs and estimators.

Item Type: Article
Divisions : Faculty of Arts and Social Sciences > School of Economics
Authors :
NameEmailORCID
Bodory, Hugo
Camponovo, Lorenzol.camponovo@surrey.ac.uk
Huber, Martin
Lechner, Michael
Date : 16 October 2018
DOI : 10.1080/07350015.2018.1476247
Copyright Disclaimer : This is an Accepted Manuscript of an article to be published by Taylor & Francis in Journal of Business and Economic Statistics in 2016, available online: http://amstat.tandfonline.com/toc/ubes20/current
Uncontrolled Keywords : Inference; Variance estimation; Treatment effects; Matching; Inverse probability weighting
Depositing User : Clive Harris
Date Deposited : 04 Jul 2018 08:26
Last Modified : 07 Nov 2018 13:37
URI: http://epubs.surrey.ac.uk/id/eprint/848642

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