演講者簡介 : 銀慶剛教授為國立清華大學統計學博士 (1996)。目前為國立台北大學統計系助理教授。其主要研究主題Stationary, Unit Root, and Long Memory Time Series Analysis及Model Selection and Forecasting in Econometrics。
演講摘要 : In this paper, two competing types of multistep predictors, the plug-in and the direct predictors, are considered in autoregressive (AR) processes. When a working model AR (k) is used for h-step prediction with h > 1, the plug-in predictor is obtained from repeatedly using the fitted (by least squares) AR(k) model with an unknown future value replaced by their own forecasts, and the direct predictor is obtained by estimating the h-step prediction model’s coefficients directly by linear least squares. Under rather mild conditions, asymptotic expressions for the mean-squared prediction errors (MSPEs) of these two predictors are obtained in stationary cases. In addition, we also extend these results to models with deterministic time trends. Based on these expressions, performances of the plug-in and direct predictors are compared. Finally, two examples are given to illustrate that some stationary case results on these MSPEs do not generalize to the nonstationary case.
