Adaptive Estimation in Time Serise Regression Models With Heteroskedasticity of Unknown Form
研究了多元时间序列回归模型中残差存在未知形式异方差和序列相关时，使用核回归和谱方法得到的GLS估计量具有自适应性，即其渐近分布与已知真实异方差和序列相关时的GLS估计量相同，并进行了蒙特卡洛实验。
In a multiple time series regression model the residuals are heteroskedastic and serially correlated of unknown form. GLS estimates of the regression coefficients using kernel regression and spectral methods are shown to be adaptive, in the sense of having the same asymptotic distribution, to the first order, as GLS estimates based on knowledge of the actual heteroskedasticity and serial correlation. A Monte Carlo experiment about the performance of our estimator is described.