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First-order serial correlation in seemingly unrelated regressions

Author

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  • Bertrand Koebel

    (BETA - Bureau d'Économie Théorique et Appliquée - INRA - Institut National de la Recherche Agronomique - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique)

Abstract

This note shows that the positive definiteness of the variance matrix of seemingly unrelated residuals with first order serial correlation implies that the roots of the serial correlation matrix have a modulus smaller than 1.

Suggested Citation

  • Bertrand Koebel, 2004. "First-order serial correlation in seemingly unrelated regressions," Post-Print hal-03336124, HAL.
    • Handle: RePEc:hal:journl:hal-03336124
    DOI: 10.1016/j.ecolet.2003.10.001
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    References listed on IDEAS

    as
    1. Koebel, Bertrand & Falk, Martin & Laisney, Francois, 2003. "Imposing and Testing Curvature Conditions on a Box-Cox Cost Function," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(2), pages 319-335, April.
    2. Berndt, Ernst R & Savin, N Eugene, 1975. "Estimation and Hypothesis Testing in Singular Equation Systems with Autoregressive Disturbances," Econometrica, Econometric Society, vol. 43(5-6), pages 937-957, Sept.-Nov.
    3. MaCurdy, Thomas E, 1981. "An Empirical Model of Labor Supply in a Life-Cycle Setting," Journal of Political Economy, University of Chicago Press, vol. 89(6), pages 1059-1085, December.
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    Cited by:

    1. Alan T. K. Wan & Jinhong You & Riquan Zhang, 2016. "A Seemingly Unrelated Nonparametric Additive Model with Autoregressive Errors," Econometric Reviews, Taylor & Francis Journals, vol. 35(5), pages 894-928, May.
    2. Amaresh Tiwari & Franz Palm, 2011. "Nonlinear Panel Data Models with Expected a Posteriori Values of Correlated Random Effects," CREPP Working Papers 1113, Centre de Recherche en Economie Publique et de la Population (CREPP) (Research Center on Public and Population Economics) HEC-Management School, University of Liège.

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    Keywords

    System of regressions; Panel data;

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