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Bootstrap Methods for Inference with Cluster-Sample IV Models

Author

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  • Keith Finlay

    (Department of Economics Tulane University New Orleans)

  • Leandro M. Magnusson

    (University of Western Australia)

Abstract

Microeconomic data often have within-cluster dependence. This dependence affects standard error estimation and inference in regression models, including the instrumental variables model. Standard corrections assume that the number of clusters is large, but when this is not the case, Wald and weak-instrument-robust tests can be severely over-sized. We examine the use of bootstrap methods to construct appropriate critical values for these tests when the number of clusters is small. We find that variants of the wild bootstrap perform well and reduce absolute size bias significantly, independent of instrument strength or cluster size. We also provide guidance in the choice among possible weak-instrument-robust tests when data have cluster dependence. These results are applicable to fixed-effects panel data models.

Suggested Citation

  • Keith Finlay & Leandro M. Magnusson, 2014. "Bootstrap Methods for Inference with Cluster-Sample IV Models," Economics Discussion / Working Papers 14-12, The University of Western Australia, Department of Economics.
    • Handle: RePEc:uwa:wpaper:14-12
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    References listed on IDEAS

    as
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    Cited by:

    1. Wang, Wenjie & Zhang, Yichong, 2024. "Wild bootstrap inference for instrumental variables regressions with weak and few clusters," Journal of Econometrics, Elsevier, vol. 241(1).
    2. 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.
    3. Christian Traxler & Carsten Burhop, 2010. "Poverty and crime in 19th century Germany: A reassessment," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2010_35, Max Planck Institute for Research on Collective Goods.

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