Testing the Homoskedasticity/Heteroskedasticity of the Errors Using the White Test: Pattern Classification by k-Variances and Informational Criteria

  • Daniel CiuiuEmail author
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 31)


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 words

Homoskedasticity 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.


  1. 1.
    Ciuiu, D.: Pattern classification using polynomial and linear regression. In: Proceedings of the International Conference Trends and Challenges in Applied Mathematics, 20–23 June 2007. Technical University of Civil Engineering, Bucharest, Romania, pp. 153–156 (2007)Google Scholar
  2. 2.
    Ciuiu, D.: Informational criteria for the homoskedasticity of the errors. Rom. J. Econ. Forecast. XIII(2), 231–244 (2010)Google Scholar
  3. 3.
    Dumitrache, I., Constantin, N., Drăgoicea, M.: Reţele Neurale. Identificarea şi Managementul proceselor (English: Neural Networks. Identification and Management of the Processes). Matrix Rom, Bucureşti (1999)Google Scholar
  4. 4.
    Jula, D.: Introducere în Econometrie (English: Introduction to Econometrics). Professional Consulting, Bucureşti (2003)Google Scholar
  5. 5.
    Păltineanu, G., Matei, P., Mateescu, G.D.: Analiză Numerică (English: Numerical Analysys). Conspress, Bucureşti (2010)Google Scholar
  6. 6.
    Popescu, Th.: Serii de Timp. Aplicaţii în Analiza Sistemelor (English: Time Series. Application to Analysis of the Systems). Efitura Tehnică, Bucureşti (2000)Google Scholar
  7. 7.
    Saporta, G.: Probabilités, Analyse des Donées et Statistique. Editions Technip, Paris (1990)Google Scholar
  8. 8.
    The Statistical Section of National Bank of Romania. Monthly Bulletin from June 2007, December 2007, June 2008, December 2008, December 2009 and December 2010. (Romanian). Accessed on December 2010
  9. 9.
    Văduva, I.: Modele de Simulare (English: Simulation Models). Editura Universităţii Bucureşti (2004)Google Scholar
  10. 10.
    Voineagu V. et al.: Teorie şi Practică Econometrică (English: Econometric Theory and Practice). Meteor Press, Bucureşti (2007)Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Technical University of Civil Engineering, BucharestBucharestRomania
  2. 2.Romanian Institute for Economic ForecastingBucharestRomania

Personalised recommendations