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.
Volume (Year): (2010)
Issue (Month): 2 (July)
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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Ciuiu, Daniel, 2008. "Pattern Classification Using Secondary Components Perceptron and Economic Applications," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 5(2), pages 51-66, June.
- Ciuiu, Daniel, 2008. "Pattern classification using principal components regression," MPRA Paper 15360, University Library of Munich, Germany.
- Ciuiu, Daniel, 2008. "Pattern classification using polynomial and linear regression," MPRA Paper 15355, University Library of Munich, Germany.
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