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Model averaging estimation of panel data models with many instruments and boosting
Hao Hao1, Bai Huang2, Tae-Hwy Lee3
1Global Data, Insight & Analytics, Ford Motor Company, Dearborn, MI, USA.
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 Boosting 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.
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