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Empirical Likelihood Ratio Test for Seemingly Unrelated Regression Models

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  • Chuanhua Wei
  • Xiaoxiao Ma

Abstract

This paper considers the problem of testing independence of equations in a seemingly unrelated regression model. A novel empirical likelihood test approach is proposed, and under the null hypothesis it is shown to follow asymptotically a chi-square distribution. Finally, simulation studies and a real data example are conducted to illustrate the performance of the proposed method.

Suggested Citation

  • Chuanhua Wei & Xiaoxiao Ma, 2021. "Empirical Likelihood Ratio Test for Seemingly Unrelated Regression Models," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(3), pages 1-1, June.
    • Handle: RePEc:ibn:ijspjl:v:10:y:2021:i:3:p:1
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    References listed on IDEAS

    as
    1. Dufour, Jean-Marie & Khalaf, Lynda, 2002. "Exact tests for contemporaneous correlation of disturbances in seemingly unrelated regressions," Journal of Econometrics, Elsevier, vol. 106(1), pages 143-170, January.
    2. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 239-253.
    3. Baltagi, Badi H. & Griffin, James M., 1983. "Gasoline demand in the OECD : An application of pooling and testing procedures," European Economic Review, Elsevier, vol. 22(2), pages 117-137, July.
    4. Tsay, Wen-Jen, 2004. "Testing for contemporaneous correlation of disturbances in seemingly unrelated regressions with serial dependence," Economics Letters, Elsevier, vol. 83(1), pages 69-76, April.
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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