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

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 $L_2$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.

KEYWORDS:

• FE-2SLS
• weak endogeneity
• combined estimator
• many instruments
• $L_2$Boosting
• FE-2SLS-Boosting

Disclosure statement

The authors declare no competing interests. This article does not contain any studies with human participants performed by any of the authors. This paper represents the opinions of the authors, and not meant to represent the opinions of Ford Motor Company.

Notes

1 Data are available at http://qed.econ.queensu.ca/jae/2014-v29.3/baltagi-li/.