Publication: Estimating Learning Models with Experimental Data
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2000
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Universidad Pompeu Fabra. Departamento de Economia y Empresa
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.