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On Developing Ridge Regression Parameters: A Graphical investigation

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Author Info

  • Muniz, Gisela

    (International University)

  • Kibria, B. M.Golam

    (International University)

  • Shukur, Ghazi

    (Jönköping University)

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    Abstract

    In this paper we have reviewed some existing and proposed some new estimators for estimating the ridge parameter "k" . All in all 19 different estimators have been studied. The investigation has been carried out using Monte Carlo simulations. A large number of different models were investigated where the variance of the random error, the number of variables included in the model, the correlations among the explanatory variables, the sample size and the unknown coefficients vectors "beta" have been varied. For each model we have performed 2000 replications and presented the results both in term of figures and tables. Based on the simulation study, we found that increasing the number of correlated variable, the variance of the random error and increasing the correlation between the independent variables have negative effect on the MSE. When the sample size increases the MSE decreases even when the correlation between the independent variables and the variance of the random error are large. In all situations, the proposed estimators have smaller MSE than the ordinary least squared and some other existing estimators.

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    Bibliographic Info

    Paper provided by HUI Research in its series HUI Working Papers with number 29.

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    Length: 25 pages
    Date of creation: 01 May 2009
    Date of revision:
    Handle: RePEc:hhs:huiwps:0029

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    Related research

    Keywords: Linear Model; LSE; MSE; Monte Carlo simulations; Multicollinearity; Ridge Regression;

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    1. Alkhamisi, M.A. & Shukur, Ghazi, 2007. "Developing Ridge Parameters for SUR Models," Working Paper Series in Economics and Institutions of Innovation 80, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
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