In other to study the complex simultaneous relationships existing in forest/tree growth modeling, six estimation methods of a simultaneous equation model are examined to determine how they cope with varying degrees of correlation between pairs of random deviates using average parameter estimates. A two-equation simultaneous system assumed covariance matrix was considered. The model was structured to have a mutual correlation between pairs of random deviates: 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 r = 0.0, 0.3 and 0.5. The performances of estimators considered were examined at various sample sizes (N = 20, 25, 30) and correlation levels with 50 replications for each. Using the average of parameter estimates criterion, 2 3SLIML were the best estimators followed by FIML and OLS for the three cases studied. Also, as sample size increases from 20 to 25 to 30, 2-3SLIML performed best and was most consistent.