In this paper, we consider a linear regression model when relevant regressors are omitted. We derive the explicit formulae for the predictive mean squared errors (PMSEs) of the Stein-rule (SR) estimator, the positive-part Stein-rule (PSR) estimator, the minimum mean squared error (MMSE) estimator, and the adjusted minimum mean squared error (AMMSE) estimator. It is shown analytically that the PSR estimator dominates the SR estimator in terms of PMSE even when there are omitted relevant regressors. Also, our numerical results show that the PSR estimator and the AMMSE estimator have much smaller PMSEs than the ordinary least squares estimator even when the relevant regressors are omitted.

Consider a linear regression model with some relevant regressors are unobservable. In such a situation, we estimate the model by using the proxy variables as regressors or by simply omitting the relevant regressors. In this paper, we derive the explicit formula of the predictive mean squared error (PMSE) of the Stein-rule (SR) estimator and the positivepart Stein-rule (PSR) estimator for the regression coefficients when the proxy variables are used. We examine the effect of using the proxy variables on the risk performances of the SR and PSR estimators. It is shown analytically that the PSR estimator dominates the SR estimator even when the proxy variables are used. Also, our numerical results show that using the proxy variables is preferable to omitting the relevant regressors.

In this paper, we consider a heterogeneous pre-test estimator which consists of the two-stage hierarchial information (2SHI) estimator and the Stein-rule (SR) estimator. This estimator is called the pre-test 2SHI (PT-2SHI) estimator. It is shown analytically that the PT-2SHI estimator dominates the SR estimator in terms of mean squared error (MSE) if the parameter values in the PT-2SHI estimator are chosen appropriately. Moreover, our numerical results show that the appropriate PT-2SHI estimator dominates the positive-part Stein-rule (PSR) estimator.

In this paper we consider to test the hypothesis using the empirical likelihood. To calculate the critical value of the test, two bootstrap methods are applied. Our simulation results indicate that the bootstrap methods improve the small sample property of the test.

In this paper, using the asymmetric LINEX loss function we derive and numerically evaluate the risk function of the new feasible ridge regression estimator.We also examine the risk performance of this estimator when the LINEX loss function is used.