An iterative feasible minimum mean squared error estimator of the disturbance variance in linear regression under asymmetric loss

In this article, we consider the risk performance of an iterative feasible minimum mean squared error estimator of the regression disturbance variance under the LINEX loss function. This loss is a generalisation of the quadratic loss function allowing for asymmetry. Notwithstanding the justification for using the feasible minimum mean squared error estimator in estimating the regression coefficients, it is found that the corresponding estimator of the disturbance variance does not, in general, improve over a class of conventional estimators commonly used in practice.