Arbeitspapier
Validity of Wild Bootstrap Inference with Clustered Errors
We study asymptotic inference based on cluster-robust variance estimators for regression models with clustered errors, focusing on the wild cluster bootstrap and the ordinary wild bootstrap. We state conditions under which both asymptotic and bootstrap tests and confidence intervals will be asymptotically valid. These conditions put limits on the rates at which the cluster sizes can increase as the number of clusters tends to infinity. To include power in the analysis, we allow the data to be generated under sequences of local alternatives. Simulation experiments illustrate the theoretical results and show that all methods can work poorly in certain cases.
- Language
-
Englisch
- Bibliographic citation
-
Series: Queen's Economics Department Working Paper ; No. 1383
- Classification
-
Wirtschaft
Statistical Simulation Methods: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
- Subject
-
Clustered data
cluster-robust variance estimator
CRVE
inference
wild bootstrap
wild cluster bootstrap
- Event
-
Geistige Schöpfung
- (who)
-
Djogbenou, Antoine
MacKinnon, James G.
Nielsen, Morten Ørregaard
- Event
-
Veröffentlichung
- (who)
-
Queen's University, Department of Economics
- (where)
-
Kingston (Ontario)
- (when)
-
2017
- Handle
- Last update
-
10.03.2025, 11:45 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the 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
- Djogbenou, Antoine
- MacKinnon, James G.
- Nielsen, Morten Ørregaard
- Queen's University, Department of Economics
Time of origin
- 2017
Other Objects (12)
Reworking Wild Bootstrap Based Inference for Clustered Errors
Asymptotic Theory and Wild Bootstrap Inference with Clustered Errors
Bootstrap-based improvements for inference with clustered errors
Improvements in bootstrap inference
Wild bootstrap and asymptotic inference with multiway clustering
Wild Bootstrap Randomization Inference for Few Treated Clusters
Wild Bootstrap Inference for Wildly Different Cluster Sizes
Fast and Wild: Bootstrap Inference in Stata Using boottest
Bootstrap, Wild Bootstrap and Generalized Bootstrap
Bootstrap inference in semiparametric generalized additive models
Bootstrap and Asymptotic Inference with Multiway Clustering
Wild Bootstrap Versus Moment-Oriented Bootstrap
Reworking Wild Bootstrap Based Inference for Clustered Errors
Asymptotic Theory and Wild Bootstrap Inference with Clustered Errors
Bootstrap-based improvements for inference with clustered errors
Improvements in bootstrap inference
Wild bootstrap and asymptotic inference with multiway clustering
Wild Bootstrap Randomization Inference for Few Treated Clusters
Wild Bootstrap Inference for Wildly Different Cluster Sizes
Fast and Wild: Bootstrap Inference in Stata Using boottest
Bootstrap, Wild Bootstrap and Generalized Bootstrap
Bootstrap inference in semiparametric generalized additive models
Bootstrap and Asymptotic Inference with Multiway Clustering
Wild Bootstrap Versus Moment-Oriented Bootstrap
Reworking Wild Bootstrap Based Inference for Clustered Errors
Asymptotic Theory and Wild Bootstrap Inference with Clustered Errors
Bootstrap-based improvements for inference with clustered errors
Improvements in bootstrap inference
Wild bootstrap and asymptotic inference with multiway clustering
Wild Bootstrap Randomization Inference for Few Treated Clusters
Wild Bootstrap Inference for Wildly Different Cluster Sizes
Fast and Wild: Bootstrap Inference in Stata Using boottest
Bootstrap, Wild Bootstrap and Generalized Bootstrap
Bootstrap inference in semiparametric generalized additive models
Bootstrap and Asymptotic Inference with Multiway Clustering