Information and entropy econometrics
Subtitle: a review and synthesis
By: Amos Golan
Series: Foundations and trends in econometrics -- v. 2, no. 1-2; Foundations and trends in econometrics -- v. 2, no. 1-2.
Publisher: Now
Published: 2008
Place of publication: Boston
Language: eng
Format: [electronic resource] :
Number of pages: 149
ISBN-13: 9781601981059
Subjects: Maximum entropy method; Statistics; Econometrics; BUSINESS & ECONOMICS; Estimation theory
Description: The overall objectives of this review and synthesis are to study the basics of information-theoretic methods in econometrics, to examine the connecting theme among these methods, and to provide a more detailed summary and synthesis of the sub-class of methods that treat the observed sample moments as stochastic. Within the above objectives, this review focuses on studying the inter-connection between information theory, estimation, and inference. To achieve these objectives, it provides a detailed survey of information-theoretic concepts and quantities used within econometrics. It also illustrates the use of these concepts and quantities within the subfield of information and entropy econometrics while paying special attention to the interpretation of these quantities. The relationships between information-theoretic estimators and traditional estimators are discussed throughout the survey. This synthesis shows that in many cases information-theoretic concepts can be incorporated within the traditional likelihood approach and provide additional insights into the data processing and the resulting inference.
Notes: Includes bibliographical references (p. 137-149).

Description based on print version record.
Table of contents: title: Abstract; title: 1. Introductory statement, motivation, and objective; title: 2. Historical perspective; title: 3. Information and entropy, background, definitions, and examples; title: 4. The classical maximum entropy principle; title: 5. Information-theoretic methods of estimation: I: Basics and zero moments; title: 6. Information-theoretic methods of estimation: II: Stochastic moments; title: 7. IT, likelihood and inference, synthesis via a discrete choice, matrix balancing example; title: 8. Concluding remarks and related work not surveyed; title: References; title: Updates.