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- W3193958746 abstract "Seemingly Unrelated Regression (SUR) model which takes cognizance of correlations and strength ofassociation between the error variables to yield more efficient estimates is a common tool in multi-equationregression analysis. The use of Cholesky method to partition the variance-covariance matrix ? into lower andupper triangular matrices to establish contemporaneous relationship among equations through their errors wasinvestigated in this paper. Literature on the efficiency of SUR and Ordinary Least Squares (OLS) estimatorsassumed inconsequential the use of either upper or lower triangular matrices from a decomposed variancecovariancematrix. This study investigated the sensitivity of the two triangular matrices on SUR and OLSestimators. A Monte Carlo experiment was performed on a four-equation model with sample sizes n = 10, 30,50, 100, 500 and 1000 and each replicated 10000 times. The Average Mean Square Error (AMSE) was used toassess the performance of the estimators. It was observed that the upper triangular matrix had higher AMSEvalues than the lower triangular matrix for SUR and OLS estimators. Also, the AMSE of SUR estimator waslower than that of OLS estimator, irrespective of the triangular matrix used." @default.
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- W3193958746 date "2013-01-01" @default.
- W3193958746 modified "2023-09-27" @default.
- W3193958746 title "Cholesky Decomposition of Variance-Covariance Matrix Effect on the Estimators of Seemingly Unrelated Regression Model" @default.
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