Effects of Multicollinearity and Correlation between the Error Terms on Some Estimators in a System of Regression Equations

Authors

  • Olanrewaju, Samuel Olayemi

Keywords:

Abstract

One of the assumptions of a single equation model is that there is one -way causation between the dependent variable Y and the independent variables X When the assumption is not valid as in many econometric models of lack of correlation between the independent variables and the error terms U is further violated Ordinary Least Square estimator would no longer efficient that was why this study examined the effects of multicollinearity and a correlation between the error terms on the performance of seven estimators and identified the estimator that yields the most preferred estimates under the separate or joint influence of the two correlation effects under consideration A twoequation model in which the two correlation problems were introduced was used in this study The error terms of the two equations were also correlated The levels of correlation between the error terms and multicollinearity were specified between -1 and 1 at an interval of 0 2 except when the correlation value approached unity A Monte Carlo experiment of 1000 trials was carried out at five levels of sample sizes 20 30 50 100 and 250 at two runs The seven estimation methods namely Ordinary Least Squares OLS Cochran Orcutt CORC Maximum Likelihood Estimator MLE Multivariate Regression MR Full Information Maximum Likelihood FIML Seemingly Unrelated Regression Model SUR and Three-Stage Least Squares 3SLS and their performances were thoroughly checked by subjecting the results obtained from each finite properties of the estimators into a multi-factor ANOVA model The significant factors of the results were further examined using their estimated marginal means and the Least Significant Difference LSD methodology to determine the best estimator The results when there is no correlation show that the OLS CORC and MLE estimators are generally preferred Furthermore the estimators of MR FIML SUR and 3SLS are preferred for computing all the parameters of the model in the presence of multicollinearity and correlation between the error terms at all the sample sizes chosen

How to Cite

Olanrewaju, Samuel Olayemi. (2020). Effects of Multicollinearity and Correlation between the Error Terms on Some Estimators in a System of Regression Equations. Global Journal of Science Frontier Research, 20(F4), 77–94. Retrieved from https://journalofscience.org/index.php/GJSFR/article/view/2729

Effects of Multicollinearity and Correlation between the Error Terms on  Some Estimators in a System of Regression Equations

Published

2020-03-15