Estimating learning models from experimental data

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Cabrales, Antonio; Garcia Fontes, Walter. Estimating learning models from experimental data. 2000
http://hdl.handle.net/10230/996
To cite or link this document: http://hdl.handle.net/10230/996
dc.contributor.author Cabrales, Antonio
dc.contributor.author Garcia Fontes, Walter
dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
dc.date.issued 2000-09-01
dc.identifier.uri http://hdl.handle.net/10230/996
dc.description.abstract We study the statistical properties of three estimation methods for a model of learning that is often fitted to experimental data: quadratic deviation measures without unobserved heterogeneity, and maximum likelihood with and without unobserved heterogeneity. After discussing identification issues, we show that the estimators are consistent and provide their asymptotic distribution. Using Monte Carlo simulations, we show that ignoring unobserved heterogeneity can lead to seriously biased estimations in samples which have the typical length of actual experiments. Better small sample properties are obtained if unobserved heterogeneity is introduced. That is, rather than estimating the parameters for each individual, the individual parameters are considered random variables, and the distribution of those random variables is estimated.
dc.language.iso eng
dc.relation.ispartofseries Economics and Business Working Papers Series; 501
dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.title Estimating learning models from experimental data
dc.type info:eu-repo/semantics/workingPaper
dc.date.modified 2014-06-03T07:14:02Z
dc.subject.keyword Microeconomics
dc.subject.keyword estimation methods
dc.subject.keyword learning
dc.subject.keyword unobserved heterogeneity
dc.subject.keyword leex
dc.rights.accessRights info:eu-repo/semantics/openAccess


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