Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/141465
Authors: 
Bodory, Hugo
Camponovo, Lorenzo
Huber, Martin
Lechner, Michael
Year of Publication: 
2016
Series/Report no.: 
IZA Discussion Papers 9706
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 design, which is based on large scale labor market data from Germany and varies w.r.t. treatment selectivity, effect heterogeneity, the share of treated, and the sample size. The results suggest that in general, the bootstrap procedures dominate the asymptotic ones in terms of size and power for both matching and weighting estimators. Furthermore, the results are qualitatively quite robust across the various simulation features.
Subjects: 
inference
variance estimation
treatment effects
matching
inverse probability weighting
JEL: 
C21
Document Type: 
Working Paper

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