Model Averaging Estimation of Panel Data Models with Many Instruments and Boosting

Hao Hao (), Bai Huang () and Tae Hwy Lee
Additional contact information
Hao Hao: Ford Motor Company
Bai Huang: Central University of Finance and Economics

No 202212, Working Papers from University of California at Riverside, Department of Economics

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 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.

Keywords: FE-2SLS; weak endogeneity; combined estimator; many instruments; L2Boosting; FE-2SLS-Boosting (search for similar items in EconPapers)
JEL-codes: C13 C33 C36 C52 (search for similar items in EconPapers)
Pages: 23 Pages
Date: 2022-07
New Economics Papers: this item is included in nep-ecm
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Forthcoming in Journal of Applied Statistics

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https://economics.ucr.edu/repec/ucr/wpaper/202212.pdf First version, 2022 (application/pdf)

Related works:
Journal Article: Model averaging estimation of panel data models with many instruments and boosting (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:202212

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