Robust Tests for Heteroscedasticity in a general Framework

Marie Lebreton and Anne Peguin-Feissolle

Annals of Economics and Statistics, 2007, issue 85, 159-187

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.

Date: 2007
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Persistent link: https://EconPapers.repec.org/RePEc:adr:anecst:y:2007:i:85:p:159-187

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