and Ghazi Shukur
M.A. Alkhamisi: Department of Mathematics, Salahaddin University, Kurdistan-Region, Iraq
Ghazi Shukur: Departments of Economics and Statistics, Jönköping International Business School (JIBS), Sweden
Abstract: In this paper, a number of procedures have been proposed for developing new biased estimators of seemingly unrelated regression (SUR) parameters, when the explanatory variables are affected by multicollinearity. Several ridge parameters are proposed and then compared in terms of the trace mean squared error (TMSE) and(PR) criterion. The PR is the proportion of replication (out of 1,000) for which the SUR version of the generalised least squares, (SGLS) estimator has a smaller TMSE than the others. The study has been made using Monte Carlo simulations where the number of equations in the system, number of observations, correlation among equations and correlation between explanatory variables have been varied. For each model we performed 1,000 replications. Our results show that under certain conditions the performance of the multivariate regression estimators based on SUR ridge parameters RSarith, RSqarith and RSmax are 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.
28 pages, January 31, 2007
Full text files
Questions (including download problems) about the papers in this series should be directed to Vardan Hovsepyan ()
Report other problems with accessing this service to Sune Karlsson ().
This page generated on 2018-01-23 23:31:21.