Estimation under Multicollinearity: A Comparative Approach Using Monte Carlo Methods

Abstract

Problem statement: A comparative investigation was done experimentally for 6 different Estimation Techniques of a just-identified simultaneous three-equation econometric model with three multi-collinear exogenous variables. Approach: The aim is to explore in depth the effects of the problems of multicollinearity and examine the sensitivity of findings to increasing sample sizes and increasing number of replications using the mean and total absolute bias statistics. Results: Findings revealed that the estimates are virtually identical for three estimators: LIML, 2SLS and ILS, while the performances of the other categories are not uniformly affected by the three levels of multicollinearity considered. It was also observed that while the frequency distribution of average parameter estimates was rather symmetric under the OLS, the other estimators was either negatively or positively skewed with no clear pattern. Conclusion: The study had established that L2ILS estimators are best for estimating parameters of data plagued by the lower open interval negative level of multicollinearity while FIML and OLS respectively rank highest for estimating parameters of data characterized by closed interval and upper categories level of multicollinearity.