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Full Description
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.
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, and 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.
Contents
1. Introductory Statement, Motivation and Objective
2. Historical Perspective
3. Information and Entropy - Background, Definitions and Examples
4. The Classical Maximum Entropy Principle
5. Information Theoretic Methods of Estimation - I: Basics and Zero Moments
6. Information Theoretic Methods of Estimation - II: Stochastic Moments
7. IT, Likelihood and Inference - Synthesis via a Discrete Choice, Matrix Balancing Example
8. Concluding Remarks and Related Work Not Surveyed
Bibliography
