Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/324375 
Year of Publication: 
2025
Series/Report no.: 
IZA Discussion Papers No. 17977
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
We provide identification results for a broad class of learning models in which continuous outcomes depend on three types of unobservables: known heterogeneity, initially unknown heterogeneity that may be revealed over time, and transitory uncertainty. We consider a common environment where the researcher only has access to a short panel on choices and realized outcomes. We establish identification of the outcome equation parameters and the distribution of the unobservables, under the standard assumption that unknown heterogeneity and uncertainty are normally distributed. We also show that, absent known heterogeneity, the model is identified without making any distributional assumption. We then derive the asymptotic properties of a sieve MLE estimator for the model parameters, and devise a tractable profile likelihood based estimation procedure. Our estimator exhibits good finite-sample properties. Finally, we illustrate our approach with an application to ability learning in the context of occupational choice. Our results point to substantial ability learning based on realized wages.
Subjects: 
occupational choice
identification
heterogeneity
learning
JEL: 
C14
C23
D83
J24
Document Type: 
Working Paper

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