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https://hdl.handle.net/10419/22244
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DC Field | Value | Language |
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dc.contributor.author | Papalia, Rosa Bernardini | en |
dc.date.accessioned | 2009-01-29T14:55:04Z | - |
dc.date.available | 2009-01-29T14:55:04Z | - |
dc.date.issued | 2003 | - |
dc.identifier.pi | urn:nbn:de:kobv:11-10050297 | en |
dc.identifier.uri | http://hdl.handle.net/10419/22244 | - |
dc.description.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. | en |
dc.language.iso | eng | en |
dc.publisher | |aHumboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes |cBerlin | en |
dc.relation.ispartofseries | |aSFB 373 Discussion Paper |x2003,29 | en |
dc.subject.ddc | 330 | en |
dc.subject.keyword | Generalized Maximum Entropy | en |
dc.subject.keyword | Generalized Cross Entropy | en |
dc.subject.keyword | Repeated Samples | en |
dc.subject.keyword | Microeconometric models | en |
dc.subject.stw | Stichprobenverfahren | en |
dc.subject.stw | Entropie | en |
dc.subject.stw | Mikroökonometrie | en |
dc.subject.stw | Theorie | en |
dc.title | Modeling the Learning from Repeated Samples: A Generalized Cross Entropy Approach | - |
dc.type | Working Paper | en |
dc.identifier.ppn | 379257238 | en |
dc.rights | http://www.econstor.eu/dspace/Nutzungsbedingungen | en |
dc.identifier.repec | RePEc:zbw:sfb373:200329 | en |
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