Eruygur, H. Ozan (2005): Generalized maximum entropy (GME) estimator: formulation and a monte carlo study.

PDF
MPRA_paper_12459.pdf Download (272kB)  Preview 
Abstract
The origin of entropy dates back to 19th century. In 1948, the entropy concept as a measure of uncertainty was developed by Shannon. A decade after in 1957, Jaynes formulated Shannon’s entropy as a method for estimation and inference particularly for illposed problems by proposing the so called Maximum Entropy (ME) principle. More recently, Golan et al. (1996) developed the Generalized Maximum Entropy (GME) estimator and started a new discussion in econometrics. This paper is divided into two parts. The first part considers the formulation of this new technique (GME). Second, by Monte Carlo simulations the estimation results of GME will be discussed in the context of nonnormal disturbances.
Item Type:  MPRA Paper 

Original Title:  Generalized maximum entropy (GME) estimator: formulation and a monte carlo study 
Language:  English 
Keywords:  Entropy, Maximum Entropy, ME, Generalized Maximum Entropy, GME, Monte Carlo Experiment, Shannon’s Entropy, Nonnormal disturbances 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C10  General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General C  Mathematical and Quantitative Methods > C0  General > C01  Econometrics 
Item ID:  12459 
Depositing User:  H. Ozan Eruygur 
Date Deposited:  02. Jan 2009 02:08 
Last Modified:  12. Feb 2013 17:58 
References:  Jaynes, E. T. (1957). "Information Theory and Statistical Mechanics." Physics Review, 106, 620630. Golan, A., Judge, G. and Miller D. (1996), Maximum Entropy Econometrics: Robust Estimation With Limited Data, John Wiley & Sons. Kapur, J.N., & Kesavan, H.K. (1992), Entropy Optimization Principles with Applications, Academic Press, London. Mittelhammer, R. and S. Cardell (1997), “On the Consistency and Asymptotic Normality of the Data Constrained GME Estimator of the GML”, Working Paper, Washington State University, Pullman, WA. Mittelhammer R., G. Judge, and D. Miller (2000), Econometric Foundations, Cambridge University Press. Mittelhammer, R., S. Cardell and L. Marsh T. (2002), “The Data Constrained GME Estimator of the GML: Asymptotic Theory and Inference”, Working Paper, Washington State University, Pullman, WA. Pukelsheim. F. (1994). “The Three Sigma Rule”, American Statistician, 48, 8891. 
URI:  http://mpra.ub.unimuenchen.de/id/eprint/12459 