Estimating Learning Models with Experimental Data

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dc.contributor.author Cabrales, Antonio
dc.contributor.author García-Fontes, Walter
dc.date.accessioned 2009-01-22T11:46:57Z
dc.date.available 2009-01-22T11:46:57Z
dc.date.issued 2000
dc.identifier.uri http://hdl.handle.net/10016/3482
dc.description.abstract We study the statistical properties of three estimation methods for a model of learning that is often tted to experimental data: quadratic deviation measures without unobserved heterogeneity, and maximum likelihood with and without unobserved heterogeneity. After discussing identi cation 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.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Universidad Pompeu Fabra. Departamento de Economia y Empresa
dc.relation.ispartofseries Working Paper;
dc.relation.ispartofseries 2000-501
dc.title Estimating Learning Models with Experimental Data
dc.type workingPaper
dc.relation.publisherversion http://www.recercat.net/bitstream/2072/646/1/501.pdf
dc.subject.eciencia Economía
dc.rights.accessRights openAccess
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