Model averaging estimation of panel data models with many instruments and boosting

Hao Hao, Bai Huang and Tae Hwy Lee

Journal of Applied Statistics, 2024, vol. 51, issue 1, 53-69

Abstract: Applied researchers often confront two issues when using the fixed effect-two-stage least squares (FE-2SLS) estimator for panel data models. One is that it may lose its consistency due to too many instruments. The other is that the gain of using FE-2SLS may not exceed its loss when the endogeneity is weak. In this paper, an $ L_{2} $ L2Boosting regularization procedure for panel data models is proposed to tackle the many instruments issue. We then construct a Stein-like model-averaging estimator to take advantage of FE and FE-2SLS-Boosting estimators. Finite sample properties are examined in Monte Carlo and an empirical application is presented.

Date: 2024
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DOI: 10.1080/02664763.2022.2114432

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