Semiparametric Generalized Least Squares in the Multivariate Nonlinear Regression Model
针对未知形式的异方差性，提出一种基于非参数最近邻估计条件方差矩阵的广义最小二乘估计量，并通过蒙特卡洛实验验证其渐近有效性。
Asymptotically efficient estimates for the multiple equations nonlinear regression model are obtained in the presence of heteroskedasticity of unknown form. The proposed estimator is a generalized least squares based on nonparametric nearest neighbor estimates of the conditional variance matrices. Some Monte Carlo experiments are reported.