Working Paper Series in Economics and Institutions of Innovation,
Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies

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.

Keywords: Multicollinearity; SUR ridge regression; Monte Carlo simulations; biased estimators; Generalized least squares

JEL-codes: C30; C51; C52

28 pages, January 31, 2007

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