 Jackknife estimation of a cluster-sample IV regression model with many weak instrumentsJohn C. Chao, Norman R. Swanson and Tiemen Woutersen Journal of Econometrics, 2023, vol. 235, issue 2, 1747-1769 Abstract: This paper proposes new jackknife IV estimators that are robust to the effects of many weak instruments and error heteroskedasticity in a cluster sample setting with cluster-specific effects and possibly many included exogenous regressors. The estimators that we propose are designed to properly partial out the cluster-specific effects and included exogenous regressors while preserving the re-centering property of the jackknife methodology. To the best of our knowledge, our proposed procedures provide the first consistent estimators under many weak instrument asymptotics in the setting considered. We also present results on the asymptotic normality of our estimators and show that t-statistics based on said estimators are asymptotically normal under the null and consistent under fixed alternatives. Monte Carlo results show that our t-statistics perform better in controlling size in finite samples than those based on alternative jackknife IV procedures previously introduced in the literature. Keywords: Cluster sample; Instrumental variables; Heteroskedasticity; Jackknife; Many weak instruments; Panel data (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:235:y:2023:i:2:p:1747-1769 DOI: 10.1016/j.jeconom.2022.12.011 Access Statistics for this article Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson More articles in Journal of Econometrics from Elsevier |
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