Jackknife estimation of a cluster-sample IV regression model with many weak instruments
| Autor: | Norman R. Swanson, Tiemen Woutersen, John C. Chao |
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| Rok vydání: | 2023 |
| Předmět: |
Statistics::Theory
History Heteroscedasticity Economics and Econometrics Polymers and Plastics Applied Mathematics Null (mathematics) Instrumental variable Estimator Asymptotic distribution Regression analysis Industrial and Manufacturing Engineering Econometrics Statistics::Methodology Cluster sampling Business and International Management Jackknife resampling Mathematics |
| Zdroj: | Journal of Econometrics. |
| ISSN: | 0304-4076 |
| DOI: | 10.1016/j.jeconom.2022.12.011 |
| Popis: | 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 our estimators are asymptotically normal under the null and consistent under fixed alternatives. Our Monte Carlo results further 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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