SEMIPARAMETRIC GENERALIZED LEAST-SQUARES IN THE MULTIVARIATE NONLINEAR-REGRESSION MODEL

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.