arXiv 31 Aug 2021 · Econometrics · publishedJournal of Econometrics (2024) · 6 citations (OpenAlex)
arXiv:2108.13707 · PDF · DOI · OpenAlex · Extracted main text
We study the wild bootstrap inference for instrumental variable regressions in the framework of a small number of large clusters in which the number of clusters is viewed as fixed and the number of observations for each cluster diverges to infinity. We first show that the wild bootstrap Wald test, with or without using the cluster-robust covariance estimator, 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. Then, we establish the required number of strong clusters for the test 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 up to a small error even under weak or partial identification in all clusters. We illustrate the good finite sample performance of the new inference methods using simulations and provide an empirical application to a well-known dataset about US local labor markets.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Autor, D., D. Dorn, and G. H. Hanson (2013) The China syndrome: Local labor market effects of import competition in the United States | 1.000 | 19 | 4 | 100% |
| 2 | Canay, I. A., A. Santos, and A. M. Shaikh (2021) The wild bootstrap with a “small" number of “large" clusters | 1.000 | 14 | 7 | 100% |
| 3 | Bester, C. A., T. G. Conley, and C. B. Hansen (2011) Inference with dependent data using cluster covariance estimators | 1.000 | 12 | 5 | 100% |
| 4 | MacKinnon, J. G., M. . Nielsen, and M. D. Webb (2023) a): Cluster-robust inference: A guide to empirical practice | 1.000 | 9 | 4 | 100% |
| 5 | Djogbenou, A. A., J. G. MacKinnon, and M. . Nielsen (2019) Djogbenou-Mackinnon-Nielsen(2019)Asymptotic theory and wild bootstrap inference with clustered errors | 1.000 | 8 | 3 | 100% |
| 6 | Finlay, K. and L. M. Magnusson (2019) Finlay-Magnusson(2019)Two applications of wild bootstrap methods to improve inference in cluster-IV models | 1.000 | 7 | 4 | 100% |
| 7 | Hwang, J (2021) Simple and trustworthy cluster-robust GMM inference | 1.000 | 6 | 3 | 100% |
| 8 | MacKinnon, J. G (2023) Fast cluster bootstrap methods for linear regression models | 1.000 | 6 | 3 | 100% |
| 9 | Canay, I. A., J. P. Romano, and A. M. Shaikh (2017) Randomization tests under an approximate symmetry assumption | 1.000 | 5 | 5 | 100% |
| 10 | Andrews, I., J. H. Stock, and L. Sun (2019) Andrews-Stock-Sun(2019)Weak instruments in instrumental variables regression: Theory and practice | 1.000 | 5 | 3 | 100% |
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