Bayesian Analysis of Correlated Regressors in Seemingly Unrelated Regression Model
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Abstract
Multicollinearity is known to result in inefficiency of estimators. This study introduces the use of Bayesian estimator to handle the problem of multicollinearity in Seemingly Unrelated Regression (SUR) model. Results of Bayesian method of estimation were compared with classical methods of estimation namely; SUR and Ordinary Least Squares (OLS) estimators when the regressors are correlated through a M. The Mean Squared Error (MSE) criterion was used to facilitate comparison among these estimators. The results revealed that the Bayesian method outperformed both SUR and OLS estimators for all sample sizes and levels of correlation considered.
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