基于有限样本的可行广义最小二乘法——在解决虚假回归问题中的应用

FGLS Method Based on Finite Samples

本文在Choi等(2008)的基础上,研究样本量有限时,可行广义最小二乘(FGLS)法在解决虚假回归问题时的表现。通过蒙特卡罗模拟实验,发现FGLS方法可以有效消除单位根序列及平稳自相关序列间的虚假回归现象,但在单位根序列长度较小时,表现不佳。本文还以研究沪、深股市指数间关联关系为例,对差分普通最小二乘回归法和FGLS两种建模方法进行比较,结果表明当样本量足够大时,在动态预测精度的评价标准下,FGLS方法更好。

On the basis of Choi et al. （2008）, this paper analyzes the performance of the feasible generalized least square （FGLS） method in Solving Spurious Regression Problems with finite samples. Through Monte Carlo simulations, we find that the FGLS method can effectively eliminate the spurious regression phenomenon, but if the length of unit root sequences is small, and its performance is poor. In addition, with the example of studying the Shanghai and Shenzhen stock market index relationship, we compare the differential ordinary least squares regression method and FGLS method, the results show that when the sample size is large enough, FGLS method is better under the dynamic forecast accuracy evaluation criteria.