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DTSTART:20220327T010000
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DTSTART;TZID=Europe/Helsinki:20220620T160000
DTEND;TZID=Europe/Helsinki:20220620T171500
DTSTAMP:20260711T181350
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SUMMARY:Arnaud Maurel (Duke University) - "Heterogeneity\, Uncertainty and Learning: A Semiparametric Identification Analysis"
DESCRIPTION:Microeconometrics Seminar CREST-PSE \nTime: 4:00 pm – 5:15 pm\nDate: 20th of June 2022\nRoom : 3105\n\nArnaud Maurel (Duke University) – “Heterogeneity\, Uncertainty and Learning: A Semiparametric Identification Analysis”\n\nAbstract: In this paper\, we provide new semiparametric identification results for a general class of learning model in which outcomes of interest depend on i) predictable heterogeneity\, ii) initially unpredictable heterogeneity that may be revealed over time\, and iii) transitory uncertainty. We consider a common environment where the researcher only has access to longitudinal data on choices and outcomes. We establish point-identification of the outcome equation parameters and the distribution of the three types of unobservables\, under the standard assumption that unpredictable heterogeneity and uncertainty are normally distributed. We also show that a pure learning model remains identified without making any distributional assumption. We then derive a sieve MLE estimator for the model parameters\, which is shown to exhibit good finite-sample performances and is very tractable in practice.          Co-authors: J. Bunting and P. Diegert .\n\n  \nOrganizers:\n \nXavier d’Haultfoeuille – CREST/ENSAE \nPhilipp Ketz – CNRS/PSE \n  \nSponsors:\nCREST \n\n
URL:https://crest.science/event/arnaud-maurel-duke-university-heterogeneity-uncertainty-and-learning-a-semiparametric-identification-analysis/
CATEGORIES:Applied Seminar,Economics,Microeconometrics,Seminars
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