EFFICIENT SEMIPARAMETRIC SEEMINGLY UNRELATED QUANTILE REGRESSION ESTIMATION
提出一种高效半参数估计方法，用于多元线性回归模型（每方程有条件分位数约束），在误差条件分布未知时，估计量渐近等价于已知最优工具变量。模拟表明，若误差条件相依，该方法优于逐方程效率校正。
We propose an efficient semiparametric estimator for the coefficients of a multivariate linear regression model—with a conditional quantile restriction for each equation—in which the conditional distributions of errors given regressors are unknown. The procedure can be used to estimate multiple conditional quantiles of the same regression relationship. The proposed estimator is asymptotically as efficient as if the true optimal instruments were known. Simulation results suggest that the estimation procedure works well in practice and dominates an equation-by-equation efficiency correction if the errors are dependent conditional on the regressors.