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Modified Ridge Parameters for Seemingly Unrelated Regression Model

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  • Zeebari , Zangin

    (Departments of Economics and Statistics)

  • Shukur , Ghazi

    (Departments of Economics and Statistics)

  • Kibria, B. M. Golam

    (Florida International University)

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    Abstract

    In this paper, we modify a number of new biased estimators of seemingly unrelated regression (SUR) parameters which are developed by Alkhamisi and Shukur (2008), AS, when the explanatory variables are affected by multicollinearity. Nine ridge parameters have been modified and compared in terms of the trace mean squared error (TMSE) and (PR) criterion. The results from this extended study are the also compared with those founded by AS. A simulation study has been conducted to compare the performance of the modified ridge parameters. The results showed that under certain conditions the performance of the multivariate ridge regression estimators based on SUR ridge RMSmax is superior to other estimators in terms of TMSE and PR criterion. In large samples and when the collinearity between the explanatory variables is not high the unbiased SUR, estimator produces a smaller TMSEs.

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    Bibliographic Info

    Paper provided by HUI Research in its series HUI Working Papers with number 43.

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    Length: 21 pages
    Date of creation: 01 Oct 2010
    Date of revision:
    Handle: RePEc:hhs:huiwps:0043

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    Keywords: Multicollinearity; modified SUR ridge regression; Monte Carlo simulations; TMSE;

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    1. Vinod, Hrishikesh D, 1978. "A Survey of Ridge Regression and Related Techniques for Improvements over Ordinary Least Squares," The Review of Economics and Statistics, MIT Press, vol. 60(1), pages 121-31, February.
    2. Denzil Fiebig & Jae Kim, 2000. "Estimation and inference in sur models when the number of equations is large," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 105-130.
    3. Chib, Siddhartha & Greenberg, Edward, 1995. "Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models," Journal of Econometrics, Elsevier, vol. 68(2), pages 339-360, August.
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