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Hao Hao1, Bai Huang2, Tae-Hwy Lee3

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Summary

This study introduces Boosting regularization for panel data models to address issues with too many instruments in fixed effect-two-stage least squares (FE-2SLS). A new model-averaging estimator is proposed, improving upon existing FE and FE-2SLS methods.

Keywords:
FE-2SLSFE-2SLS-Boostingcombined estimatormany instrumentsweak endogeneity

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Area of Science:

  • Econometrics
  • Statistical Modeling
  • Panel Data Analysis

Background:

  • Fixed effect-two-stage least squares (FE-2SLS) is widely used for panel data.
  • FE-2SLS faces challenges with numerous instruments and weak endogeneity.
  • Existing methods may suffer from inconsistency or limited gains.

Purpose of the Study:

  • To propose a Boosting regularization procedure for panel data models.
  • To address the "many instruments" problem in FE-2SLS.
  • To develop a Stein-like model-averaging estimator combining FE and FE-2SLS-Boosting.

Main Methods:

  • A Boosting regularization procedure is developed for panel data.
  • A Stein-like model-averaging estimator is constructed.
  • Monte Carlo simulations and an empirical application are used for evaluation.

Main Results:

  • The proposed Boosting regularization effectively handles the many instruments issue.
  • The model-averaging estimator leverages the strengths of FE and FE-2SLS-Boosting.
  • Finite sample properties are examined, demonstrating the estimator's performance.

Conclusions:

  • The Boosting regularization procedure offers a solution for "many instruments" in FE-2SLS.
  • The Stein-like model-averaging estimator provides an improved approach for panel data analysis.
  • The findings are validated through simulations and a practical application.