Informational Criteria for the Homoscedasticity of Errors

In this paper we will test the homoscedasticity of errors using the Goldfeld-Quandt test and we will classify the points using the explanatory variable by which we sort them. We will also use the Hartley test for the equality of the class error variances (if we have at least two classes). For all the points (only one class) and all the possible classifications for which we have homoscedasticity we will compute some informational criteria like Akaike ( AIC=Akaike Informational Criterion) and Schwartz ( BIC=Bayes Informational Criterion). Of course, from these classifications we will choose that classification with the minimum of the considered criterion. As application, we have monthly data from November 1990 to November 2008 concerning the price indexes for services, the price indexes for food and for the price indexes of non-food goods.