Inference with Large Clustered Datasets

James MacKinnon

No 274691, Queen's Economics Department Working Papers from Queen's University - Department of Economics

Abstract: Inference using large datasets is not nearly as straightforward as conventional econo- metric 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. More- over, inferences become increasingly unreliable as the dataset gets larger. These asser- tions are based on an extensive series of estimations undertaken using a large dataset taken from the U.S. Current Population Survey.

Keywords: Financial; Economics (search for similar items in EconPapers)
Pages: 18
Date: 2016-09
New Economics Papers: this item is included in nep-ecm
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Related works:
Journal Article: Inference with Large Clustered Datasets (2016)
Working Paper: Inference With Large Clustered Datasets (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:quedwp:274691

DOI: 10.22004/ag.econ.274691

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