arXiv 20 Aug 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2408.10686 · PDF · DOI · OpenAlex · Extracted main text
We study the gradient wild bootstrap-based inference for instrumental variable quantile 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. For the Wald inference, we show that our wild bootstrap Wald test, with or without studentization using the cluster-robust covariance estimator (CRVE), controls size asymptotically up to a small error as long as the parameter of endogenous variable is strongly identified in at least one of the clusters. We further show that the wild bootstrap Wald test with CRVE studentization is more powerful for distant local alternatives than that without. Last, we develop a wild bootstrap Anderson-Rubin (AR) test for the weak-identification-robust inference. We show it controls size asymptotically up to a small error, even under weak or partial identification for 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 | 11 | 3 | 100% |
| 2 | Canay, I. A., A. Santos, and A. M. Shaikh (2021) The wild bootstrap with a “small" number of “large" clusters | 1.000 | 9 | 3 | 100% |
| 3 | Leung, M. P (2023) Network cluster-robust inference | 1.000 | 9 | 3 | 100% |
| 4 | Chernozhukov, V. and C. Hansen (2008) Chernozhukov-Hansen(2008a)Instrumental variable quantile regression: A robust inference approach | 1.000 | 8 | 3 | 100% |
| 5 | Wang, W. and Y. Zhang (2024) Wild bootstrap inference for instrumental variables regressions with weak and few clusters self | 1.000 | 6 | 3 | 100% |
| 6 | 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 | 5 | 3 | 100% |
| 7 | MacKinnon, J. G., M. . Nielsen, and M. D. Webb (2023) Cluster-robust inference: A guide to empirical practice | 1.000 | 5 | 3 | 100% |
| 8 | Chernozhukov, V. and C. Hansen (2006) Chernozhukov-Hansen(2006)Instrumental quantile regression inference for structural and treatment effect models | 0.923 | 14 | 4 | 79% |
| 9 | Hagemann, A (2017) Hagemann(2017)Cluster-robust bootstrap inference in quantile regression models | 0.874 | 6 | 2 | 100% |
| 10 | Bester, C. A., T. G. Conley, and C. B. Hansen (2011) Inference with dependent data using cluster covariance estimators | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 71 scored citations.