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Inference with Large Clustered Datasets

Author

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  • MacKinnon, James G.

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.

Suggested Citation

  • MacKinnon, James G., 2016. "Inference with Large Clustered Datasets," Queen's Economics Department Working Papers 274691, Queen's University - Department of Economics.
    • Handle: RePEc:ags:quedwp:274691
    DOI: 10.22004/ag.econ.274691
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    Cited by:

    1. James G. MacKinnon & Matthew D. Webb, 2020. "When and How to Deal with Clustered Errors in Regression Models," Working Paper 1421, Economics Department, Queen's University.
    2. Djogbenou, Antoine A. & MacKinnon, James G. & Nielsen, Morten Ørregaard, 2019. "Asymptotic theory and wild bootstrap inference with clustered errors," Journal of Econometrics, Elsevier, vol. 212(2), pages 393-412.
    3. Djogbenou, Antoine & MacKinnon, James G. & Orregaard Nielsen, Morten, 2017. "Validity of Wild Bootstrap Inference with Clustered Errors," Queen's Economics Department Working Papers 274709, Queen's University - Department of Economics.
    4. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Cluster-robust inference: A guide to empirical practice," Journal of Econometrics, Elsevier, vol. 232(2), pages 272-299.
    5. James G. MacKinnon, 2019. "How cluster-robust inference is changing applied econometrics," Canadian Journal of Economics, Canadian Economics Association, vol. 52(3), pages 851-881, August.
    6. Hansen, Bruce E. & Lee, Seojeong, 2019. "Asymptotic theory for clustered samples," Journal of Econometrics, Elsevier, vol. 210(2), pages 268-290.
    7. MacKinnon, James G., 2023. "Using large samples in econometrics," Journal of Econometrics, Elsevier, vol. 235(2), pages 922-926.
    8. Djogbenou, Antoine & MacKinnon, James G. & Orregaard Nielsen, Morten, 2017. "Validity of Wild Bootstrap Inference with Clustered Errors," Queen's Economics Department Working Papers 274709, Queen's University - Department of Economics.

    More about this item

    Keywords

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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