A1322
Title: Semiparametric and nonparametric instrumental variable estimation with first stage isotonic regression
Authors: Mengshan Xu - University of Mannheim (Germany) [presenting]
Taisuke Otsu - London School of Economics (United Kingdom)
Kazuhiko Shinoda - Nagoya University (Japan)
Abstract: A semiparametric and a nonparametric instrumental variable (IV) estimator are proposed under the assumption that the conditional mean of the endogenous variable, given the instrumental variable, is known to be monotone increasing. Isotonic estimation is employed to obtain the fitted instruments in the first stage of a two-stage semiparametric or nonparametric estimation procedure. The proposed semiparametric IV estimator is shown to be tuning-parameter-free and achieves the semiparametric efficiency bound. Moreover, compared to the nonparametric two-stage least squares estimator, the proposed nonparametric IV estimator requires notably fewer tuning parameters and achieves the same convergence rate. Additionally, it exhibits greater stability as evidenced by Monte Carlo simulations.
