Comparison of Efficiencies of Several Estimators for Linear Regressions With Autocorrelated Errors
比较了最大似然、迭代Prais-Winsten和迭代Cochrane-Orcutt三种方法在线性回归模型中的效率，发现前两种方法效率相当，后者因忽略初始观测值而效率较低。
Abstract This article compares several estimators for linear regression models with first-order autocorrelated errors. Asymptotic variances of the estimators of regression coefficients are obtained up to order T −2 for the maximum likelihood, the iterated Prais-Winsten, and the iterated Cochrane—Orcutt methods. It is shown that the first two methods are equivalent in efficiency and that the last is less efficient. This loss in efficiency is due to the effect of the initial observation in the regressor. Asymptotic biases of the estimators for the autocorrelation coefficient are also given.