Maximum Entropy Econometrics

Maximum Entropy Econometrics

Author: Amos Golan

Publisher: John Wiley & Sons

Published: 1996-05

Total Pages: 336

ISBN-13:

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This monograph examines the problem of recovering and processing information when the underlying data are limited or partial, and the corresponding models that form the basis for estimation and inference are ill-posed or undermined


Information and Entropy Econometrics

Information and Entropy Econometrics

Author: Amos Golan

Publisher: Now Publishers Inc

Published: 2008

Total Pages: 167

ISBN-13: 160198104X

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Information and Entropy Econometrics - A Review and Synthesis summarizes the basics of information theoretic methods in econometrics and the connecting theme among these methods. The sub-class of methods that treat the observed sample moments as stochastic is discussed in greater details. I Information and Entropy Econometrics - A Review and Synthesis -focuses on inter-connection between information theory, estimation and inference. -provides a detailed survey of information theoretic concepts and quantities used within econometrics and then show how these quantities are used within IEE. -pays special attention for the interpretation of these quantities and for describing the relationships between information theoretic estimators and traditional estimators. Readers need a basic knowledge of econometrics, but do not need prior knowledge of information theory. The survey is self contained and interested readers can replicate all results and examples provided. Whenever necessary the readers are referred to the relevant literature. Information and Entropy Econometrics - A Review and Synthesis will benefit researchers looking for a concise introduction to the basics of IEE and to acquire the basic tools necessary for using and understanding these methods. Applied researchers can use the book to learn improved new methods, and applications for extracting information from noisy and limited data and for learning from these data.


Non-Extensive Entropy Econometrics for Low Frequency Series

Non-Extensive Entropy Econometrics for Low Frequency Series

Author: Second Bwanakare

Publisher: De Gruyter Open

Published: 2017-07-24

Total Pages: 218

ISBN-13: 9783110550436

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Non-extensive Entropy Econometrics for Low Frequency Series provides a new and robust power-law-based, non-extensive entropy econometrics approach to the economic modelling of ill-behaved inverse problems. Particular attention is paid to national account-based general equilibrium models known for their relative complexity. In theoretical terms, the approach generalizes Gibbs-Shannon-Golan entropy models, which are useful for describing ergodic phenomena. In essence, this entropy econometrics approach constitutes a junction of two distinct concepts: Jayne's maximum entropy principle and the Bayesian generalized method of moments. Rival econometric techniques are not conceptually adapted to solving complex inverse problems or are seriously limited when it comes to practical implementation. Recent literature showed that amplitude and frequency of macroeconomic fluctuations do not substantially diverge from many other extreme events, natural or human-related, once they are explained in the same time (or space) scale. Non-extensive entropy is a precious device for econometric modelling even in the case of low frequency series, since outputs evolving within the Gaussian attractor correspond to the Tsallis entropy limiting case of Tsallis q-parameter around unity. This book introduces a sub-discipline called Non-extensive Entropy Econometrics or, using a recent expression, Superstar Generalised Econometrics. It demonstrates, using national accounts-based models, that this approach facilitates solving nonlinear, complex inverse problems, previously considered intractable, such as the constant elasticity of substitution class of functions. This new proposed approach could extend the frontier of theoretical and applied econometrics.


Econometrics of Information and Efficiency

Econometrics of Information and Efficiency

Author: Jati Sengupta

Publisher: Springer Science & Business Media

Published: 2013-03-14

Total Pages: 267

ISBN-13: 9401582025

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Econometrics as an applied discipline attempts to use information in a most efficient manner, yet the information theory and entropy approach developed by Shannon and others has not played much of a role in applied econometrics. Econometrics of Information and Efficiency bridges the gap. Broadly viewed, information theory analyzes the uncertainty of a given set of data and its probabilistic characteristics. Whereas the economic theory of information emphasizes the value of information to agents in a market, the entropy theory stresses the various aspects of imprecision of data and their interactions with the subjective decision processes. The tools of information theory, such as the maximum entropy principle, mutual information and the minimum discrepancy are useful in several areas of statistical inference, e.g., Bayesian estimation, expected maximum likelihood principle, the fuzzy statistical regression. This volume analyzes the applications of these tools of information theory to the most commonly used models in econometrics. The outstanding features of Econometrics of Information and Efficiency are: A critical survey of the uses of information theory in economics and econometrics; An integration of applied information theory and economic efficiency analysis; The development of a new economic hypothesis relating information theory to economic growth models; New lines of research are emphasized.


Essays on Maximum Entropy Principle with Applications to Econometrics and Finance

Essays on Maximum Entropy Principle with Applications to Econometrics and Finance

Author: Sung Yong Park

Publisher: ProQuest

Published: 2007

Total Pages: 179

ISBN-13: 9780549344216

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This dissertation studies density estimation and portfolio selection problems using the maximum entropy (ME) principle. Since an entropy measure turns out to be a distance measure between two distributions, it can be used to estimate unknown density function. Entropy can be also interpreted as a measure of the degree of diversification and thus provides an useful way to construct optimal portfolio weights. In this dissertation three subjects are studied extensively. First, we propose ME autoregressive conditional heteroskedasticity model with demonstrating how we can extract informative functional from the data in the form of moment function. Second, the portfolio selection problem is considered using ME principle. We propose to use cross entropy measure as the objective function (to minimize) with side conditions coming from the mean and variance-covariance matrix of the resampled asset returns. Finally, using ME principle, we provided characterization of some well-known income distributions and flexible parametric income distributions which satisfy certain stylized facts of personal income data. Empirical results showed that maximum entropy principle is quite useful for analyzing economic and financial data.


Maximum-entropy Models in Science and Engineering

Maximum-entropy Models in Science and Engineering

Author: Jagat Narain Kapur

Publisher: John Wiley & Sons

Published: 1989

Total Pages: 660

ISBN-13: 9788122402162

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This Is The First Comprehensive Book About Maximum Entropy Principle And Its Applications To A Diversity Of Fields Like Statistical Mechanics, Thermo-Dynamics, Business, Economics, Insurance, Finance, Contingency Tables, Characterisation Of Probability Distributions (Univariate As Well As Multivariate, Discrete As Well As Continuous), Statistical Inference, Non-Linear Spectral Analysis Of Time Series, Pattern Recognition, Marketing And Elections, Operations Research And Reliability Theory, Image Processing, Computerised Tomography, Biology And Medicine. There Are Over 600 Specially Constructed Exercises And Extensive Historical And Bibliographical Notes At The End Of Each Chapter.The Book Should Be Of Interest To All Applied Mathematicians, Physicists, Statisticians, Economists, Engineers Of All Types, Business Scientists, Life Scientists, Medical Scientists, Radiologists And Operations Researchers Who Are Interested In Applying The Powerful Methodology Based On Maximum Entropy Principle In Their Respective Fields.