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Boosting GMM with Many Instruments When Some Are Invalid and/or Irrelevant

Author

Listed:
  • Hao Hao

    (Global Data Insight & Analytics, Ford Motor Company)

  • Tae-Hwy Lee

    (Department of Economics, University of California Riverside)

Abstract

When the endogenous variable is an unknown function of observable instruments, its conditional mean can be approximated using the sieve functions of observable instruments. We propose a novel instrument selection method, Double-criteria Boosting (DB), that consistently selects only valid and relevant instruments from a large set of candidate instruments. In the Monte Carlo simulation, we compare GMM using DB (DB-GMM) with other estimation methods and demonstrate that DB-GMM gives lower bias and RMSE. In the empirical application to the automobile demand, the DB-GMM estimator is suggesting a more elastic estimate of the price elasticity of demand than the standard 2SLS estimator.

Suggested Citation

  • Hao Hao & Tae-Hwy Lee, 2025. "Boosting GMM with Many Instruments When Some Are Invalid and/or Irrelevant," Working Papers 202504, University of California at Riverside, Department of Economics.
    • Handle: RePEc:ucr:wpaper:202504
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    References listed on IDEAS

    as
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    4. Liao, Zhipeng, 2013. "Adaptive Gmm Shrinkage Estimation With Consistent Moment Selection," Econometric Theory, Cambridge University Press, vol. 29(5), pages 857-904, October.
    5. DiTraglia, Francis J., 2016. "Using invalid instruments on purpose: Focused moment selection and averaging for GMM," Journal of Econometrics, Elsevier, vol. 195(2), pages 187-208.
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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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