Information and Entropy Econometrics - A Review and Synthesis
By Amos Golan. 2008
188 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, Management Science and Operations Research, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/f87c66a3ce02c29656da9e454f8feff3349fc7bb
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In Economics and Econometrics
In Management Science and Operations Research
In Statistical and Nonlinear Physics
Citations in the corpus
- Comparing generalised maximum entropy and partial least squares methods for structural equation models (2009, methodology): “The main advantages of using the GME estimation method are its desirable properties [1,23], which can be briefly summarised in the following points:”