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Open AccessArticle

Generalized Maximum Entropy Analysis of the Linear Simultaneous Equations Model

1
School of Economic Sciences and IMPACT, Washington State University, Pullman, WA 99164, USA
2
School of Economic Sciences and Statistics, Washington State University, Pullman, WA 99164, USA
3
Salford Systems, San Diego, CA 92126, USA
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Author to whom correspondence should be addressed.
Entropy 2014, 16(2), 825-853; https://doi.org/10.3390/e16020825
Received: 20 November 2013 / Revised: 17 January 2014 / Accepted: 28 January 2014 / Published: 12 February 2014
(This article belongs to the Special Issue Maximum Entropy and Its Application)
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. View Full-Text
Keywords: robustness; asymptotic normality; consistency; hypothesis testing; Monte Carlo experiments; Wald test robustness; asymptotic normality; consistency; hypothesis testing; Monte Carlo experiments; Wald test
MDPI and ACS Style

Marsh, T.L.; Mittelhammer, R.; Cardell, N.S. Generalized Maximum Entropy Analysis of the Linear Simultaneous Equations Model. Entropy 2014, 16, 825-853.

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