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

Hao Hao () and Tae-Hwy Lee ()
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Hao Hao: Global Data Insight & Analytics, Ford Motor Company, Michigan
Tae-Hwy Lee: Department of Economics, University of California Riverside

No 202411, 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. 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.

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 C5 (search for similar items in EconPapers)
Pages: 38 Pages
Date: 2024-12
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ets
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