| Item Type |
Journal paper
|
| Abstract |
This article 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 analyze 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. |
| Authors |
Bodory, Hugo; Camponovo, Lorenzo; Huber, Martin & Lechner, Michael |
| Journal or Publication Title |
Journal of Business & Economic Statistics |
| Language |
English |
| Subjects |
economics |
| HSG Classification |
contribution to scientific community |
| HSG Profile Area |
SEPS - Economic Policy |
| Refereed |
Yes |
| Date |
January 2020 |
| Publisher |
American Statistical Association |
| Volume |
38 |
| Number |
1 |
| Page Range |
183-200 |
| Number of Pages |
18 |
| ISSN |
0735-0015 |
| ISSN-Digital |
1537-2707 |
| Publisher DOI |
https://doi.org/10.1080/07350015.2018.1476247 |
| Depositing User |
Susanne Moser
|
| Date Deposited |
29 Apr 2020 09:18 |
| Last Modified |
21 Nov 2022 13:02 |
| URI: |
https://www.alexandria.unisg.ch/publications/260145 |
The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators
