Arbeitspapier
Asymptotic Theory and 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. Under a somewhat stronger set of conditions, we also derive formal Edgeworth expansions for the asymptotic and bootstrap test statistics. Simulation experiments illustrate the theoretical results, and the Edgeworth expansions explain the overrejection of the asymptotic test and shed light on the choice of auxiliary distribution for the wild bootstrap.
- Language
-
Englisch
- Bibliographic citation
-
Series: Queen's Economics Department Working Paper ; No. 1399
- 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
Edgeworth expansion
inference
wild bootstrap
wild cluster bootstrap
- Event
-
Geistige Schöpfung
- (who)
-
Djogbenou, Antoine A.
MacKinnon, James G.
Nielsen, Morten Ørregard
- Event
-
Veröffentlichung
- (who)
-
Queen's University, Department of Economics
- (where)
-
Kingston (Ontario)
- (when)
-
2018
- Handle
- Last update
-
10.03.2025, 11:43 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
- Djogbenou, Antoine A.
- MacKinnon, James G.
- Nielsen, Morten Ørregard
- Queen's University, Department of Economics
Time of origin
- 2018
Other Objects (12)
Bootstrap and Asymptotic Inference with Multiway Clustering
Wild bootstrap and asymptotic inference with multiway clustering
Reworking Wild Bootstrap Based Inference for Clustered Errors
Bootstrap-based improvements for inference with clustered errors
Validity of Wild Bootstrap Inference with Clustered Errors
Asymptotic versus bootstrap inference for inequality indices of the cumulative distribution function
Proxy SVARs : asymptotic theory, bootstrap inference, and the effects of income tax changes in the United States
Proxy SVARs: Asymptotic theory, bootstrap inference, and the effects of income tax changes in the United States
Improvements in bootstrap inference
Asymptotic theory of statistical inference for time series
Structure and asymptotic theory for nonlinear models with GARCH errors
The Power of Bootstrap and Asymptotic Tests
Bootstrap and Asymptotic Inference with Multiway Clustering
Wild bootstrap and asymptotic inference with multiway clustering
Reworking Wild Bootstrap Based Inference for Clustered Errors
Bootstrap-based improvements for inference with clustered errors
Validity of Wild Bootstrap Inference with Clustered Errors
Asymptotic versus bootstrap inference for inequality indices of the cumulative distribution function
Proxy SVARs : asymptotic theory, bootstrap inference, and the effects of income tax changes in the United States
Proxy SVARs: Asymptotic theory, bootstrap inference, and the effects of income tax changes in the United States
Improvements in bootstrap inference
Asymptotic theory of statistical inference for time series
Structure and asymptotic theory for nonlinear models with GARCH errors
The Power of Bootstrap and Asymptotic Tests
Bootstrap and Asymptotic Inference with Multiway Clustering
Wild bootstrap and asymptotic inference with multiway clustering
Reworking Wild Bootstrap Based Inference for Clustered Errors
Bootstrap-based improvements for inference with clustered errors
Validity of Wild Bootstrap Inference with Clustered Errors
Asymptotic versus bootstrap inference for inequality indices of the cumulative distribution function
Proxy SVARs : asymptotic theory, bootstrap inference, and the effects of income tax changes in the United States
Proxy SVARs: Asymptotic theory, bootstrap inference, and the effects of income tax changes in the United States
Improvements in bootstrap inference
Asymptotic theory of statistical inference for time series
Structure and asymptotic theory for nonlinear models with GARCH errors