, B. M.Golam Kibria
and Ghazi Shukur
Gisela Muniz: International University, Postal: Department of Mathematics and Statistics, Florida Miami, Florida, USA
B. M.Golam Kibria: International University, Postal: Department of Mathematics and Statistics, Florida Miami, Florida, USA
Ghazi Shukur: Jönköping University, Postal: Department of Economics and Statistics, Jönköping, Sweden, and Centre for Labour Market Policy (CAFO), Department of Economics, and Statistics, Växjö University, Sweden
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.
25 pages, May 1, 2009
Full text files
Questions (including download problems) about the papers in this series should be directed to Helena Nilsson ()
Report other problems with accessing this service to Sune Karlsson ().
This page generated on 2018-02-05 20:50:22.