Publication:
Estimating Learning Models with Experimental Data

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dc.affiliation.dptoUC3M. Departamento de EconomĂ­aes
dc.contributor.authorCabrales, Antonio
dc.contributor.authorGarcĂ­a-Fontes, Walter
dc.date.accessioned2009-01-22T11:46:57Z
dc.date.available2009-01-22T11:46:57Z
dc.date.issued2000
dc.description.abstractWe 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.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10016/3482
dc.language.isoeng
dc.publisherUniversidad Pompeu Fabra. Departamento de Economia y Empresa
dc.relation.ispartofseriesWorking Paper;
dc.relation.ispartofseries2000-501
dc.relation.publisherversionhttp://www.recercat.net/bitstream/2072/646/1/501.pdf
dc.rights.accessRightsopen access
dc.subject.ecienciaEconomĂ­a
dc.titleEstimating Learning Models with Experimental Data
dc.typeworking paper*
dspace.entity.typePublication
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