期刊名称：Journal of Modern Applied Statistical Methods
出版年度：2006
卷号：5
期号：1
页码：10
出版社：Wayne State University
摘要：The ordinary least squares (OLS) estimates in the regression model are efficient when the disturbances have mean zero, constant variance, and are uncorrelated. In problems concerning time series, it is often the case that the disturbances are correlated. Using computer simulations, the robustness of various estimators are considered, including estimated generalized least squares. It was found that if the disturbance structure is autoregressive and the dependent variable is nonstochastic and linear or quadratic, the OLS performs nearly as well as its competitors. For other forms of the dependent variable, rules of thumb are presented to guide practitioners in the choice of estimators.
关键词：Autocorrelation; autoregressive; ordinary least squares; generalized least squares; efficiency
