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Efficient Estimation in NPIV Models: A Comparison of Various Neural Networks-Based Estimators. (arXiv:2110.06763v3 [econ.EM] UPDATED)
Jan. 7, 2022, 2:10 a.m. | Jiafeng Chen, Xiaohong Chen, Elie Tamer
cs.LG updates on arXiv.org arxiv.org
Artificial Neural Networks (ANNs) can be viewed as nonlinear sieves that can
approximate complex functions of high dimensional variables more effectively
than linear sieves. We investigate the computational performance of various
ANNs in nonparametric instrumental variables (NPIV) models of moderately high
dimensional covariates that are relevant to empirical economics. We present two
efficient procedures for estimation and inference on a weighted average
derivative (WAD): an orthogonalized plug-in with optimally-weighted sieve
minimum distance (OP-OSMD) procedure and a sieve efficient score (ES) …
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