HUI Working Papers, HUI Research
Gisela Muniz, B. M.Golam Kibria and Ghazi Shukur
On Developing Ridge Regression Parameters: A Graphical investigation
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.
Keywords: Linear Model; LSE; MSE; Monte Carlo simulations; Multicollinearity; Ridge Regression; (follow links to similar papers)
JEL-Codes: F10; (follow links to similar papers)
25 pages, May 1, 2009
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