An Unbiased Estimator of the Covariance Matrix of the Mixed Regression Estimator
推导了混合回归估计量协方差矩阵的无偏估计量，用于构建有限样本标准误，并与传统一致估计量比较，发现简单修正后近似效果良好。
Abstract This article derives an unbiased estimator of the covariance matrix of the “mixed regression” estimator suggested by Theil and Goldberger for combining prior information with the sample information in regression analysis. This derivation facilitates the construction of finite-sample standard errors for the mixed estimators of the individual regression coefficients. Comparisons are made between the unbiased covariance estimator, the conventional consistent estimator based on the generalized least squares formula, and a simple modification of the latter, which is found to approximate the unbiased estimator well in practical situations.