Heterogeneity, Uncertainty and Learning: Semiparametric Identification and Estimation

Jackson Bunting (), Paul Diegert () and Arnaud Maurel ()
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Jackson Bunting: University of Washington
Paul Diegert: Toulouse School of Economics
Arnaud Maurel: Duke University

No 17977, IZA Discussion Papers from Institute of Labor Economics (IZA)

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.

Keywords: occupational choice; identification; heterogeneity; learning (search for similar items in EconPapers)
JEL-codes: C14 C23 D83 J24 (search for similar items in EconPapers)
Date: 2025-06
New Economics Papers: this item is included in nep-dcm and nep-lma
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