A Monte Carlo simulation framework on the relative performance of system estimators in the presence of multicollinearity

The correctness and reliability of findings and\recommendations of empirical studies conducted by social and economic researchers depend largely on the efficiency of the econometrics methodologies employed in such studies. Of particular interest are such studies which are centered on the Sustainable Development Goals (SDG) considering the relevance of such studies to the total wellbeing of the world populace. In view of this, there is always a need for theoretical review of econometrics methodologies commonly used by researchers with a view to providing researchers with research updates on the theoretical standing of these methodologies. In this study, we set up a Monte Carlo Experiment (MCE) to evaluate the relative performance of various estimators of a simultaneous equation model in the presence of varied levels of multicollinearity. The model was estimated with a simulated data set of sample size 30 over 100 replications. The parameter estimates obtained from the six estimators considered were evaluated using RMSE criteria. Our result revealed that irrespective of the level of multicollinearity in our model, ILS and OLS yielded best estimates of the parameters. On the contrary, the system estimators all performed poorly in the presence of multicollinearity. Also, 2SLS, LIML and 3SLS estimators yielded virtually identical estimates. By our findings, in the presence of multicollinearity, estimators OLS and ILS performed best and should therefore be preferred above the multi-equation estimators.