Generalized Maximum Entropy Analysis of the Linear Simultaneous Equations Model

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 i...

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Bibliographic Details
Published inEntropy (Basel, Switzerland) Vol. 16; no. 2; pp. 825 - 853
Main Authors Marsh, Thomas L, Mittelhammer, Ron, Cardell, Nicholas Scott
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2014
Subjects
asymptotic normality
consistency
Entropy
Estimators
hypothesis testing
Maximum entropy
Monte Carlo experiments
Monte Carlo methods
robustness
Samples
Sampling
Simultaneous equations
Statistical methods
Wald test
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Summary: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.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e16020825