Arbeitspapier
When and how to deal with clustered errors in regression models
We discuss when and how to deal with possibly clustered errors in linear regression models. Specifically, we discuss situations in which a regression model may plausibly be treated as having error terms that are arbitrarily correlated within known clusters but uncorrelated across them. The methods we discuss include various covariance matrix estimators, possibly combined with various methods of obtaining critical values, several bootstrap procedures, and randomization inference. Special attention is given to models with few treated clusters and clusters that vary in size, where inference may be problematic. Two empirical examples and a simulation experiment illustrate the methods we discuss and the concerns we raise.
- Sprache
-
Englisch
- Erschienen in
-
Series: Queen’s Economics Department Working Paper ; No. 1421
- Klassifikation
-
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
- Thema
-
clustered data
cluster-robust variance estimator
CRVE
wild cluster bootstrap
robust inference
- Ereignis
-
Geistige Schöpfung
- (wer)
-
MacKinnon, James G.
Webb, Matthew
- Ereignis
-
Veröffentlichung
- (wer)
-
Queen's University, Department of Economics
- (wo)
-
Kingston (Ontario)
- (wann)
-
2019
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- MacKinnon, James G.
- Webb, Matthew
- Queen's University, Department of Economics
Entstanden
- 2019
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