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dc.contributor.authorPapalia, Rosa Bernardinien
dc.date.accessioned2009-01-29T14:55:04Z-
dc.date.available2009-01-29T14:55:04Z-
dc.date.issued2003-
dc.identifier.piurn:nbn:de:kobv:11-10050297en
dc.identifier.urihttp://hdl.handle.net/10419/22244-
dc.description.abstractIn 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.isoengen
dc.publisher|aHumboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes |cBerlinen
dc.relation.ispartofseries|aSFB 373 Discussion Paper |x2003,29en
dc.subject.ddc330en
dc.subject.keywordGeneralized Maximum Entropyen
dc.subject.keywordGeneralized Cross Entropyen
dc.subject.keywordRepeated Samplesen
dc.subject.keywordMicroeconometric modelsen
dc.subject.stwStichprobenverfahrenen
dc.subject.stwEntropieen
dc.subject.stwMikroökonometrieen
dc.subject.stwTheorieen
dc.titleModeling the Learning from Repeated Samples: A Generalized Cross Entropy Approach-
dc.typeWorking Paperen
dc.identifier.ppn379257238en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:sfb373:200329en

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