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One-sided tests for independence of seemingly unrelated regression equations

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  • Kurata, Hiroshi
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    Abstract

    In a seemingly unrelated regression model with p([greater-or-equal, slanted]2) equations, this paper considers the problem of testing independence of equations against a one-sided alternative hypothesis. The power functions of invariant tests are evaluated and the locally most mean powerful invariant test is obtained.

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    File URL: http://www.sciencedirect.com/science/article/B6WK9-4B0X54P-6/2/7f326ad6eef724fd8f27b7f655a109ee
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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 90 (2004)
    Issue (Month): 2 (August)
    Pages: 393-406

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    Handle: RePEc:eee:jmvana:v:90:y:2004:i:2:p:393-406

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    Related research

    Keywords: Invariance One-sided hypothesis Locally most mean powerful test Seemingly unrelated regression model Independence;

    References

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    1. Maxwell King & Ping Wu, 1997. "Locally optimal one-sided tests for multiparameter hypotheses," Econometric Reviews, Taylor & Francis Journals, vol. 16(2), pages 131-156.
    2. Breusch, T S & Pagan, A R, 1980. "The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics," Review of Economic Studies, Wiley Blackwell, vol. 47(1), pages 239-53, January.
    3. Kariya, Takeaki & Fujikoshi, Yasunori & Krishnaiah, P. R., 1984. "Tests for independence of two multivariate regression equations with different design matrices," Journal of Multivariate Analysis, Elsevier, vol. 15(3), pages 383-407, December.
    4. Kurata, Hiroshi, 1999. "On the Efficiencies of Several Generalized Least Squares Estimators in a Seemingly Unrelated Regression Model and a Heteroscedastic Model," Journal of Multivariate Analysis, Elsevier, vol. 70(1), pages 86-94, July.
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