Heterogeneity, Uncertainty and Learning: Semiparametric Identification and Estimation

Jackson Bunting, Paul Diegert and Arnaud Maurel

No 32164, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We provide semiparametric identification results for a broad class of learning models in which continuous outcomes depend on three types of unobservables: i) known heterogeneity, ii) initially unknown heterogeneity that may be revealed over time, and iii) 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 three types of 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. Monte Carlo simulation results indicate that our estimator exhibits good finite-sample properties.

JEL-codes: C14 C50 D83 (search for similar items in EconPapers)
Date: 2024-02
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Working Paper: Heterogeneity, Uncertainty and Learning: Semiparametric Identification and Estimation (2024)
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