Paper #501

Título:

Estimating learning models from experimental data

Autores:

Antonio Cabrales y Walter Garcia Fontes

Data:

Septiembre 2000

Resumen:

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.

Palabras clave:

Estimation methods, learning, unobserved heterogeneity, Leex

Códigos JEL:

C51, C91, D83

Área de investigación:

Microeconomía

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