Inference With Large Clustered Datasets

James MacKinnon

No 1365, Working Paper from Economics Department, Queen's University

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.

Keywords: placebo laws; cluster-robust inference; earnings equation; wild cluster bootstrap; CPS data; sample size (search for similar items in EconPapers)
JEL-codes: C12 C15 C18 C21 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2016-09
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (14)

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https://www.econ.queensu.ca/sites/econ.queensu.ca/files/qed_wp_1365.pdf First version 2016 (application/pdf)

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Journal Article: Inference with Large Clustered Datasets (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:qed:wpaper:1365

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