Testing the Homoskedasticity/Heteroskedasticity of the Errors Using the White Test: Pattern Classification by k-Variances and Informational Criteria
In this paper we will test the homoskedasticity/heteroskedasticity of the errors for a linear regression model using the White homoskedasticity test. In the case of heteroskedasticity we use the k-variances algorithm to classify the data such that all the classes are homoskedastic. The informational criteria analogues to the Akaike and Schwartz criteria are used to choose the best classification.
Key wordsHomoskedasticity k-variances Informational criteria
This paper is supported by the Sectorial Operational Programme Human Resources Development (SOP HRD) financed from the European Social Fund and by the Romanian Government under the contract number SOP HRD/89/1.5/S/62988.
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