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A Heteroskedasticity Test Robust to Conditional Mean Misspecification

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  • Lee, Byung-Joo

Abstract

This paper proposes a new test statistic to deter the presence of heteroskedasticity. The proposed test does not require a parametric specification of the mean regression function in the first stage regression. The regression function is estimated nonparametrically by the kernel estimation method. The nonparametric residual is estimated and used as a proxy for the random disturbance term. This nonparametric residual is robust to regression function misspecification. Asymptotic normality is established using extensions of classical U-statistic theorems. The test statistic is computed using the nonparametric quantities, but the resulting inference has a standard chi-square distribution. Copyright 1992 by The Econometric Society.

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  • Lee, Byung-Joo, 1992. "A Heteroskedasticity Test Robust to Conditional Mean Misspecification," Econometrica, Econometric Society, vol. 60(1), pages 159-171, January.
  • Handle: RePEc:ecm:emetrp:v:60:y:1992:i:1:p:159-71
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    Cited by:

    1. Lumsdaine, Robin L. & Ng, Serena, 1999. "Testing for ARCH in the presence of a possibly misspecified conditional mean," Journal of Econometrics, Elsevier, vol. 93(2), pages 257-279, December.
    2. Bayraci, Selcuk, 2007. "Modeling the volatility of FTSE All Share Index Returns," MPRA Paper 28095, University Library of Munich, Germany.
    3. Einmahl, J.H.J. & van Keilegom, I., 2006. "Tests for Independence in Nonparametric Regression," Discussion Paper 2006-80, Tilburg University, Center for Economic Research.
    4. Wang, Kevin Q., 2002. "Nonparametric tests of conditional mean-variance efficiency of a benchmark portfolio," Journal of Empirical Finance, Elsevier, vol. 9(2), pages 133-169, March.

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