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

Author

Listed:
  • Hao Hao

    (Ford Motor Company)

  • Bai Huang

    (Central University of Finance and Economics)

  • Tae-Hwy Lee

    (Department of Economics, University of California Riverside)

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.

Suggested Citation

  • Hao Hao & Bai Huang & Tae-Hwy Lee, 2022. "Model Averaging Estimation of Panel Data Models with Many Instruments and Boosting," Working Papers 202212, University of California at Riverside, Department of Economics.
    • Handle: RePEc:ucr:wpaper:202212
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    File URL: https://economics.ucr.edu/repec/ucr/wpaper/202212.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    FE-2SLS; weak endogeneity; combined estimator; many instruments; L2Boosting; FE-2SLS-Boosting;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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