Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/149091
Authors: 
MacKinnon, James G.
Year of Publication: 
2016
Series/Report no.: 
Queen's Economics Department Working Paper 1365
Abstract: 
Inference using large datasets is not nearly as straightforward as conventional econometric theory suggests when the disturbances are clustered, even with very small intra-cluster correlations. The information contained in such a dataset grows much more slowly with the sample size than it would if the observations were independent. Moreover, inferences become increasingly unreliable as the dataset gets larger. These assertions are based on an extensive series of estimations undertaken using a large dataset taken from the U.S. Current Population Survey.
Subjects: 
cluster-robust inference
earnings equation
wild cluster bootstrap
CPS data
sample size
placebo laws
JEL: 
C12
C15
C18
C21
Document Type: 
Working Paper

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