On Performance of Simultaneous Equation Model Estimators Using Average Parameter Estimates in the Presence of Correlated Random Deviates

This study examined how six estimation methods of a simultaneous equation model cope with varying degrees of correlation between pairs of random deviates using the Average of parameter estimates. A two-equation simultaneous system was considered with assumed covariance matrix. The model was structured to have a mutual correlation between pairs of random deviates, which is a violation of the assumption of mutual independence between pairs of such random deviates. The correlation between the pairs of normal deviates were generated using three scenarios of r = 0.0, 0.3 and 0.5. The performances of various estimators considered were examined at various sample sizes, correlation levels and 50 replications. The sample size, N = 20, 25, 30 each replicated 50 times was considered. Using the Average of parameter estimates criterion, 2-3SLIML are the best estimators followed by Full Information Maximum Likelihood and by Ordinary Least Squares for the three cases studied. Also, as the sample size increases from 20 to 25 and 30, 2-3SLIML still performed the best (i.e., 2-3SLIML is consistent).