Arbeitspapier
Wild Bootstrap Randomization Inference for Few Treated Clusters
When there are few treated clusters in a pure treatment or difference-in-differences setting, t tests based on a cluster-robust variance estimator (CRVE) can severely over-reject. Although procedures based on the wild cluster bootstrap often work well when the number of treated clusters is not too small, they can either over-reject or under-reject seriously when it is. In a previous paper, we showed that procedures based on randomization inference (RI) can work well in such cases. However, RI can be impractical when the number of possible randomizations is small. We propose a bootstrap-based alternative to randomization inference, which mitigates the discrete nature of RI P values in the few-clusters case. We also compare it to two other procedures. None of them works perfectly when the number of clusters is very small, but they can work surprisingly well.
- Language
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Englisch
- Bibliographic citation
-
Series: Queen's Economics Department Working Paper ; No. 1404
- Classification
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Wirtschaft
Hypothesis Testing: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
- Subject
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CRVE
grouped data
clustered data
panel data
wild cluster bootstrap
difference-in-differences
DiD
randomization inference
- Event
-
Geistige Schöpfung
- (who)
-
MacKinnon, James G.
Webb, Matthew
- Event
-
Veröffentlichung
- (who)
-
Queen's University, Department of Economics
- (where)
-
Kingston (Ontario)
- (when)
-
2018
- Handle
- Last update
-
10.03.2025, 11:45 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- MacKinnon, James G.
- Webb, Matthew
- Queen's University, Department of Economics
Time of origin
- 2018
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