Robust tests for heteroscedasticity in a general framework

Marie Lebreton and Anne Peguin-Feissolle ()
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Marie Lebreton: GREThA - Groupe de Recherche en Economie Théorique et Appliquée - UB - Université de Bordeaux - CNRS - Centre National de la Recherche Scientifique
Anne Peguin-Feissolle: GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique

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Abstract: In this paper, we suggest two heteroscedasticity tests that require little knowledge of the functional relationship determining the variance. The first one is based on a Taylor series expansion of the unknown scedastic function and the second one is based on artificial neural networks. These tests are easy to apply and perform well in our small-sample simulations, but they possess asymptotically incorrect sizes except in the case of normal errors. Therefore, we propose a simple modification in order to correct this non-robustness property. We investigate the size and the power of these tests by Monte Carlo experiments by comparing them to well-known heteroscedasticity tests.

Keywords: heteroscedasticity (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (4)

Published in Annales d'Economie et de Statistique, 2007, 85, pp.159-187. ⟨10.2307/20079184⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-00390142

DOI: 10.2307/20079184

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