Wild bootstrap inference for instrumental variables regressions with weak and few clusters

Wenjie Wang and Yichong Zhang

Journal of Econometrics, 2024, vol. 241, issue 1

Abstract: We study the wild bootstrap inference for instrumental variable regressions under an alternative asymptotic framework that the number of independent clusters is fixed, the size of each cluster diverges to infinity, and the within cluster dependence is sufficiently weak. We first show that the wild bootstrap Wald test controls size asymptotically up to a small error as long as the parameters of endogenous variables are strongly identified in at least one of the clusters. Second, we establish the conditions for the bootstrap tests to have power against local alternatives. We further develop a wild bootstrap Anderson–Rubin test for the full-vector inference and show that it controls size asymptotically even under weak identification in all clusters. We illustrate their good performance using simulations and provide an empirical application to a well-known dataset about US local labor markets.

Keywords: Wild bootstrap; Weak instrument; Clustered data; Randomization test (search for similar items in EconPapers)
JEL-codes: C12 C26 C31 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:241:y:2024:i:1:s0304407624000733

DOI: 10.1016/j.jeconom.2024.105727

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