Para acceder a los documentos con el texto completo, por favor, siga el siguiente enlace:

Estimating learning models from experimental data
Cabrales, Antonio; Garcia Fontes, Walter
Universitat Pompeu Fabra. Departament d'Economia i Empresa
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 withand 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 areobtained 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.
estimation methods
unobserved heterogeneity
L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
Documento de trabajo

Mostrar el registro completo del ítem