Inference with Large Clustered Datasets

James MacKinnon

L'Actualité Economique, 2016, vol. 92, issue 4, 649-665

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.

Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
http://id.erudit.org/iderudit/1040501ar Full text (text/html)

Related works:
Working Paper: Inference With Large Clustered Datasets (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ris:actuec:0157

Access Statistics for this article

L'Actualité Economique is currently edited by Benoit Dostie

More articles in L'Actualité Economique from Société Canadienne de Science Economique Contact information at EDIRC.
Bibliographic data for series maintained by Benoit Dostie ( this e-mail address is bad, please contact ).