Most mean powerful test for testing heteroscedastic disturbances in the linear regression model

This paper investigates the application of the most mean powerful test to the problem of testing for heteroscedastic disturbances in the linear regression model. The most mean powerful test was introduced by Begum and King [5] and is based on the generalized neyman-pearson lemma. This test provides an optimal test of certain composite hypotheses. Previous applications have only involved testing problems whose null hypotheses, after reduction through invariance arguments, are one-dimensional (see, Begum and King [5]) and two dimensional (see, Begum and King [6]). This is the first application involving the problem of testing heteroscedastic disturbances in the linear regression model. A Monte Carlo simulation experiment was conducted to assess the small sample performances of the test with encouraging results.