Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/97471 
Erscheinungsjahr: 
2014
Schriftenreihe/Nr.: 
Queen's Economics Department Working Paper No. 1314
Verlag: 
Queen's University, Department of Economics, Kingston (Ontario)
Zusammenfassung: 
The cluster robust variance estimator (CRVE) relies on the number of clusters being large. The precise meaning of 'large' is ambiguous, but a shorthand 'rule of 42' has emerged in the literature. We show that this rule depends crucially on the assumption of equal-sized clusters. Monte Carlo evidence suggests that rejection frequencies at the five percent level can be more than twice the desired size when a dataset has 50 clusters proportional to the populations of the US states. In contrast, using a cluster wild bootstrap procedure for the same dataset usually results in very accurate rejection frequencies. We also show that, when the test regressor is a dummy variable, both conventional and bootstrap tests perform badly when the proportion of clusters treated is very small or very large. A third set of simulations uses placebo laws to see whether similar results hold in a difference-in-differences framework.
Schlagwörter: 
CRVE
grouped data
clustered data
panel data
cluster wild bootstrap
difference in differences
effective number of clusters
placebo laws
JEL: 
C15
C21
C23
Dokumentart: 
Working Paper

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