Instrumental Variables Estimation With Flexible Distributions
探讨利用非线性矩条件和灵活参数族误差分布来提高工具变量估计的效率，蒙特卡洛模拟显示在厚尾或偏斜误差分布下效率显著提升。
Instrumental variables are often associated with low estimator precision. This article explores efficiency gains that might be achievable using moment conditions that are nonlinear in the disturbances and are based on flexible parametric families for error distributions. We show that these estimators can achieve the semiparametric efficiency bound when the true error distribution is a member of the parametric family. Monte Carlo simulations demonstrate low efficiency loss in the case of normal error distributions and potentially significant efficiency improvements in the case of thick-tailed and/or skewed error distributions.