ESTIMATING TRENDING VARIABLES IN THE PRESENCE OF FRACTIONALLY INTEGRATED ERRORS
研究了线性回归模型中误差项为分数整合时，普通最小二乘和一次差分估计量的性质，发现一次差分估计量在误差非平稳时能提高收敛速度并消除伪回归。
This paper considers the problems of estimation and inference in the linear regression model with fractionally integrated errors. The ordinary least squares (OLS) and the first differenced (FD) estimators are studied. Relative to the OLS estimators, a substantial increase in the convergence rates of the coefficient estimator for the stochastic regressor can be achieved by the FD estimators when the error term is nonstationary. However, the preceding decisive results can not always sustain when the error term is stationary. We also find that the FD estimators can eliminate the spurious regression because the FD t -ratio for the coefficient estimators never diverges.