Adaptive Estimation in Time Serise Regression Models With Heteroskedasticity of Unknown Form

Abstract

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.