Small-Sample Properties of Simultaneous Equation Estimators with Multicollinearity

Abstract

A Monte Carlo simulation is employed to reinvestigate the general disagreement in the literature regarding the small-sample properties of simultaneous equation estimators under widely different levels of multicollinearity. This disagreement has been attributed to the greater use by more complex estimators of information on variables excluded from each equation, producing the advantage in inference of increasing precision at low levels of collinearity, but the disadvantage of greater estimator sensitivity with higher collinearity. Although simulation results indicate improvement of some less complex techniques relative to their more complex counterparts as collinearity rises, extreme collinearity is required to generate substantial reversals in relative performance.

Key Words:

• Small-sample estimation
• Monte Carlo experiment
• Full-information estimators
• Limited-information estimators
• Non-parametric tests
• Estimator sensitivity