In this paper, we examine the small sample properties of
R2 based on the Stein-rule estimator of coefficients (say,
R2S) when relevant regressors are omitted in a specified model. The following is shown, when the model is correctly specified, the bias of
R2S is smaller than that of
R2 based on the OLS estimator (say,
R2S), and the mean square error (MSE) of
R2S is smaller than or comparable with that of
R20. But, as the magnitude of specification error increases, both bias and MSE of
R2S become larger than those of
R20.
抄録全体を表示