A Comparison of the Stein-Rule and Positive-Part Stein-Rule Estimators in a Misspecified Linear Regression Model
A Comparison of the Stein-Rule and Positive-Part Stein-Rule Estimators in a Misspecified Linear Regression Model
复制标题
错误指定的线性回归模型中斯坦因规则估计器和正部分斯坦因规则估计器的比较
作者：
K. Ohtani
在本文中，我们检查了在指定模型中省略相关回归量时 Steinrule (SR) 和正部分 Stein-rule (PSR) 估计器的预测风险的性能。推导了PSR估计器预测风险的精确公式，并给出了在规范误差下PSR估计器优于SR估计器的充分条件。数值计算表明，即使有遗漏变量，PSR 估计器似乎也是 OLS、SR 和 PSR 估计器中的最佳选择。
In this paper, we examine the performance of the predictive risk of the Steinrule (SR) and positive-part Stein-rule (PSR) estimators when relevant regressors are omitted in the specified model. The exact formula of the predictive risk of the PSR estimator is derived, and the sufficient condition for the PSR estimator to dominate the SR estimator under a specification error is given. It is shown by numerical computation that the PSR estimator seems to be the best choice among the OLS, SR, and PSR estimators even when there are omitted variables.