Generalized Maximum Entropy Analysis of the Linear Simultaneous Equations Model

Abstract

A generalized maximum entropy estimator is developed for the linear simultaneous equations model. Monte Carlo sampling experiments are used to evaluate the estimator’s performance in small and medium sized samples, suggesting contexts in which the current generalized maximum entropy estimator is superior in mean square error to two and three stage least squares. Analytical results are provided relating to asymptotic properties of the estimator and associated hypothesis testing statistics. Monte Carlo experiments are also used to provide evidence on the power and size of test statistics. An empirical application is included to demonstrate the practical implementation of the estimator.

Keywords

• robustness
• asymptotic normality
• consistency
• hypothesis testing
• Monte Carlo experiments
• Wald test