Developing Ridge Parameters for SUR Models
AbstractIn 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.
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Bibliographic InfoPaper provided by Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies in its series Working Paper Series in Economics and Institutions of Innovation with number 80.
Length: 28 pages
Date of creation: 31 Jan 2007
Date of revision:
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Multicollinearity; SUR ridge regression; Monte Carlo simulations; biased estimators; Generalized least squares;
Find related papers by JEL classification:
- C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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