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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, Michigan)

  • 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 DBGMM 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, 2024. "Boosting GMM with Many Instruments When Some Are Invalid and/or Irrelevant," Working Papers 202411, University of California at Riverside, Department of Economics.
    • Handle: RePEc:ucr:wpaper:202411
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    File URL: https://economics.ucr.edu/repec/ucr/wpaper/202411.pdf
    File Function: First version, 2024
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    More about this item

    Keywords

    Causal inference with high dimensional instruments; Irrelevant instruments; Invalid instruments; Instrument Selection; Machine Learning; Boosting.;
    All these keywords.

    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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