Estimating Learning Models from Experimental Data
Universitat Pompeu Fabra Economics and Business Working Paper No. 501
41 Pages Posted: 14 Nov 2000
Date Written: September 2000
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: Suggested Citation