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Bootstrap and Asymptotic Inference with Multiway Clustering

Author

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  • MacKinnon, James G.
  • Orregaard Nielsen, Morten
  • Webb, Matthew D.

Abstract

We study a cluster-robust variance estimator (CRVE) for regression models with clustering in two dimensions that was proposed in Cameron, Gelbach, and Miller (2011). We prove that this CRVE is consistent and yields valid inferences under precisely stated assumptions about moments and cluster sizes. We then propose several wild bootstrap procedures and prove that they are asymptotically valid. Simulations suggest that bootstrap inference tends to be much more accurate than inference based on the t distribution, especially when there are few clusters in at least one dimension. An empirical example confirms that bootstrap inferences can differ substantially from conventional ones.

Suggested Citation

  • MacKinnon, James G. & Orregaard Nielsen, Morten & Webb, Matthew D., 2017. "Bootstrap and Asymptotic Inference with Multiway Clustering," Queen's Economics Department Working Papers 274712, Queen's University - Department of Economics.
    • Handle: RePEc:ags:quedwp:274712
    DOI: 10.22004/ag.econ.274712
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    Cited by:

    1. Harold D. Chiang & Kengo Kato & Yukun Ma & Yuya Sasaki, 2022. "Multiway Cluster Robust Double/Debiased Machine Learning," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1046-1056, June.
    2. Hansen, Bruce E. & Lee, Seojeong, 2019. "Asymptotic theory for clustered samples," Journal of Econometrics, Elsevier, vol. 210(2), pages 268-290.
    3. Harold D. Chiang & Yuya Sasaki, 2019. "Lasso under Multi-way Clustering: Estimation and Post-selection Inference," Papers 1905.02107, arXiv.org, revised Aug 2019.
    4. MacKinnon, James G. & Webb, Matthew D., 2017. "Pitfalls when Estimating Treatment Effects Using Clustered Data," Queen's Economics Department Working Papers 274713, Queen's University - Department of Economics.
    5. David Roodman & James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2019. "Fast and wild: Bootstrap inference in Stata using boottest," Stata Journal, StataCorp LLC, vol. 19(1), pages 4-60, March.
    6. Ivan A. Canay & Andres Santos & Azeem M. Shaikh, 2021. "The Wild Bootstrap with a “Small” Number of “Large” Clusters," The Review of Economics and Statistics, MIT Press, vol. 103(2), pages 346-363, May.
    7. Yusuf Emre Akgunduz & Emine Meltem Bastan & Ufuk Demiroglu & Semih Tumen, 2021. "Product‐level estimates of exchange rate pass‐through: Evidence from Turkey☆," The World Economy, Wiley Blackwell, vol. 44(7), pages 2203-2226, July.
    8. Tumen, Semih, 2021. "The effect of refugees on native adolescents’ test scores: Quasi-experimental evidence from PISA," Journal of Development Economics, Elsevier, vol. 150(C).
    9. Semih Tumen & Hakan Ulucan, 2024. "Empowered or impoverished: the impact of panic buttons on domestic violence," Review of Economics of the Household, Springer, vol. 22(4), pages 1423-1459, December.
    10. Laurent Davezies & Xavier D'Haultfoeuille & Yannick Guyonvarch, 2018. "Asymptotic results under multiway clustering," Papers 1807.07925, arXiv.org, revised Aug 2018.

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    Keywords

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    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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