Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/22244
Authors: 
Papalia, Rosa Bernardini
Year of Publication: 
2003
Series/Report no.: 
Discussion papers of interdisciplinary research project 373 2003,29
Abstract: 
In this study we illustrate a Maximum Entropy (ME) methodology for modeling incomplete information and learning from repeated samples. The basis for this method has its roots in information theory and builds on the classical maximum entropy work of Janes (1957). We illustrate the use of this approach, describe how to impose restrictions on the estimator, and how to examine the sensitivity of ME estimates to the parameter and error bounds. Our objective is to show how empirical measures of the value of information for microeconomic models can be estimated in the maximum entropy view.
Subjects: 
Generalized Maximum Entropy
Generalized Cross Entropy
Repeated Samples
Microeconometric models
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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