Arbeitspapier
How cluster-robust inference is changing applied econometrics
In many fields of economics, and also in other disciplines, it is hard to justify the assumption that the random error terms in regression models are uncorrelated. It seems more plausible to assume that they are correlated within clusters, such as geographical areas or time periods, but uncorrelated across clusters. It has therefore become very popular to use "clustered" standard errors, which are robust against arbitrary patterns of within-cluster variation and covariation. Conventional methods for inference using clustered standard errors work very well when the model is correct and the data satisfy certain conditions, but they can produce very misleading results in other cases. This paper discusses some of the issues that users of these methods need to be aware of.
- Language
-
Englisch
- Bibliographic citation
-
Series: Queen’s Economics Department Working Paper ; No. 1413
- 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
-
CRVE
grouped data
clustered data
panel data
wild cluster bootstrap
difference-in-differences
treatment model
fixed effects
- Event
-
Geistige Schöpfung
- (who)
-
MacKinnon, James G.
- Event
-
Veröffentlichung
- (who)
-
Queen's University, Department of Economics
- (where)
-
Kingston (Ontario)
- (when)
-
2019
- 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
- MacKinnon, James G.
- Queen's University, Department of Economics
Time of origin
- 2019
Other Objects (12)
Robust econometrics
Robust Econometrics
Cluster-robust inference: A guide to empirical practice
Robust Bayesian analysis for econometrics
Fast and reliable jackknife and bootstrap methods for cluster-robust inference
Impossible inference in econometrics: Theory and applications
Robust inference in deconvolution
Robust inference with clustered data
Robust inference with clustered data
Robust methods and asymptotic theory in nonlinear econometrics
Essays in Econometrics with focus on smooth minimum distance inference
Robust Bayesian inference in proxy SVARs
Robust econometrics
Robust Econometrics
Cluster-robust inference: A guide to empirical practice
Robust Bayesian analysis for econometrics
Fast and reliable jackknife and bootstrap methods for cluster-robust inference
Impossible inference in econometrics: Theory and applications
Robust inference in deconvolution
Robust inference with clustered data
Robust inference with clustered data
Robust methods and asymptotic theory in nonlinear econometrics
Essays in Econometrics with focus on smooth minimum distance inference
Robust Bayesian inference in proxy SVARs
Robust econometrics
Robust Econometrics
Cluster-robust inference: A guide to empirical practice
Robust Bayesian analysis for econometrics
Fast and reliable jackknife and bootstrap methods for cluster-robust inference
Impossible inference in econometrics: Theory and applications
Robust inference in deconvolution
Robust inference with clustered data
Robust inference with clustered data
Robust methods and asymptotic theory in nonlinear econometrics
Essays in Econometrics with focus on smooth minimum distance inference