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Inference with Large Clustered Datasets

Author

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  • James G. MacKinnon

    () (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.

Suggested Citation

  • James G. MacKinnon, 2016. "Inference with Large Clustered Datasets," Working Papers 1365, Queen's University, Department of Economics.
  • Handle: RePEc:qed:wpaper:1365
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    File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1365.pdf
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    Cited by:

    1. Antoine A. Djogbenou & James G. MacKinnon & Morten Orregard Nielsen, 2018. "Asymptotic Theory and Wild Bootstrap Inference with Clustered Errors," Working Papers 1399, Queen's University, Department of Economics.
    2. Antoine Djogbenou & James G. MacKinnon & Morten Ørregaard Nielsen, 2017. "Validity of Wild Bootstrap Inference with Clustered Errors," Working Papers 1383, Queen's University, Department of Economics.

    More about this item

    Keywords

    cluster-robust inference; earnings equation; wild cluster bootstrap; CPS data; sample size; placebo laws;

    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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