Boosting GMM with Many Instruments When Some Are Invalid or Irrelevant

Hao Hao () and Tae Hwy Lee
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Hao Hao: Ford Motor Company

No 202309, Working Papers from University of California at Riverside, Department of Economics

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. Monte Carlo compares GMM using DB with other methods such as GMM using Lasso and shows DB-GMM gives lower bias and RMSE. In the empirical application to automobile demand, the DB-GMM estimator is suggesting a more elastic estimate of the price elasticity of demand than the standard 2SLS estimator.

Keywords: Causal inference with high dimensional instruments; Irrelevant instruments; Invalid instruments; Instrument Selection; Machine Learning; Boosting. (search for similar items in EconPapers)
JEL-codes: C1 C2 C3 C5 (search for similar items in EconPapers)
Pages: 34 Pages
Date: 2023-09
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ger
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https://economics.ucr.edu/repec/ucr/wpaper/202309.pdf First version, 2023 (application/pdf)

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