Estimating Learning Models from Experimental Data

Universitat Pompeu Fabra Economics and Business Working Paper No. 501

41 Pages Posted: 14 Nov 2000

See all articles by Antonio Cabrales

Antonio Cabrales

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences

Walter Garcia-Fontes

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences

Date Written: September 2000

Abstract

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.

Keywords: Estimation Methods, Learning, Unobserved Heterogeneity

JEL Classification: C51, C91, D83

Suggested Citation

Cabrales, Antonio and Garcia-Fontes, Walter, Estimating Learning Models from Experimental Data (September 2000). Universitat Pompeu Fabra Economics and Business Working Paper No. 501. Available at SSRN: https://ssrn.com/abstract=246526 or http://dx.doi.org/10.2139/ssrn.246526

Antonio Cabrales

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences ( email )

Ramon Trias Fargas 25-27
Barcelona, 08005
Spain
(34-93) 542 27 65 (Phone)
(34-93) 542 17 46 (Fax)

Walter Garcia-Fontes (Contact Author)

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences ( email )

Ramon Trias Fargas 25-27
Barcelona, 08005
Spain

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