Developing Ridge Parameters for SUR Models
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.
|Date of creation:||31 Jan 2007|
|Date of revision:|
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- Denzil Fiebig & Jae Kim, 2000. "Estimation and inference in sur models when the number of equations is large," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 105-130.
- Vinod, Hrishikesh D, 1978. "A Survey of Ridge Regression and Related Techniques for Improvements over Ordinary Least Squares," The Review of Economics and Statistics, MIT Press, vol. 60(1), pages 121-31, February.
- Chib, Siddhartha & Greenberg, Edward, 1995. "Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models," Journal of Econometrics, Elsevier, vol. 68(2), pages 339-360, August.
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