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Testing for the appropriate level of clustering in linear regression models

Author

Listed:
  • James G. MacKinnon

    (Queen's University)

  • Morten Ørregaard Nielsen

    (Queen's University and CREATES)

  • Matthew D. Webb

    (Carleton University)

Abstract
The overwhelming majority of empirical research that uses cluster-robust inference assumes that the clustering structure is known, even though there are often several possible ways in which a dataset could be clustered. We propose two tests for the correct level of clustering in regression models. One test focuses on inference about a single coefficient, and the other on inference about two or more coefficients. We provide both asymptotic and wild bootstrap implementations. The proposed tests work for a null hypothesis of either no clustering or "fine" clustering against alternatives of "coarser" clustering. We also propose a sequential testing procedure to determine the appropriate level of clustering. Simulations suggest that the bootstrap tests perform very well under the null hypothesis and can have excellent power. An empirical example suggests that using the tests leads to sensible inferences.

Suggested Citation

  • James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2022. "Testing for the appropriate level of clustering in linear regression models," Working Paper 1428, Economics Department, Queen's University.
    • Handle: RePEc:qed:wpaper:1428
    as

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    File URL: https://www.econ.queensu.ca/sites/econ.queensu.ca/files/wpaper/qed_wp_1428.pdf
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    References listed on IDEAS

    as
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    2. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2023. "Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust," Stata Journal, StataCorp LP, vol. 23(4), pages 942-982, December.
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    33. David Roodman & James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2019. "Fast and wild: Bootstrap inference in Stata using boottest," Stata Journal, StataCorp LP, vol. 19(1), pages 4-60, March.
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    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. 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.
    2. Yong Cai, 2021. "Panel Data with Unknown Clusters," Papers 2106.05503, arXiv.org, revised Jan 2022.
    3. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2023. "Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust," Stata Journal, StataCorp LP, vol. 23(4), pages 942-982, December.
    4. Chaeho Chase Lee & Erdal Atukeren & Hohyun Kim, 2024. "Knowledge Capital and Stock Returns during Crises in the Manufacturing Sector: Moderating Role of Market Share, Tobin’s Q, and Cash Holdings," Risks, MDPI, vol. 12(6), pages 1-23, June.
    5. Paul Hufe, 2024. "The Parental Wage Gap and the Development of Socio-emotional Skills in Children," Working Papers 2024-010, Human Capital and Economic Opportunity Working Group.

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    More about this item

    Keywords

    CRVE; grouped data; clustered data; cluster-robust variance estimator; robust inference; wild bootstrap; wild cluster bootstrap;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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