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Efficient Estimation Of Average Derivatives In Npiv Models: Simulation Comparisons Of Neural Network Estimators, Jiafeng Chen, Xiaohong Chen, Elie Tamer
Efficient Estimation Of Average Derivatives In Npiv Models: Simulation Comparisons Of Neural Network Estimators, Jiafeng Chen, Xiaohong Chen, Elie Tamer
Cowles Foundation Discussion Papers
Artificial Neural Networks (ANNs) can be viewed as \emph{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) procedure. Both estimators for WAD use ANN sieves to approximate the unknown NPIV function and are root-n asymptotically normal …
Adaptive Estimation And Uniform Confidence Bands For Nonparametric Iv, Xiaohong Chen, Timothy M. Christensen, Sid Kankanala
Adaptive Estimation And Uniform Confidence Bands For Nonparametric Iv, Xiaohong Chen, Timothy M. Christensen, Sid Kankanala
Cowles Foundation Discussion Papers
We introduce computationally simple, data-driven procedures for estimation and inference on a structural function h0 and its derivatives in nonparametric models using instrumental variables. Our first procedure is a bootstrap-based, data-driven choice of sieve dimension for sieve nonparametric instrumental variables (NPIV) estimators. When implemented with this data-driven choice, sieve NPIV estimators of h0 and its derivatives are adaptive: they converge at the best possible (i.e., minimax) sup-norm rate, without having to know the smoothness of h0, degree of endogeneity of the regressors, or instrument strength. Our second procedure is a data-driven approach for constructing honest and …