Wednesday, November 1, 2017
4:00 pm

Ulric B. and Evelyn L. Bray Social Sciences Seminar

Nonparametric Analysis of Finite Mixtures
Yuichi Kitamura, Professor of Economics, Yale University

Abstract: Finite mixture models are useful in applied econometrics. They can be used to model unobserved heterogeneity, which plays major roles in labor economics, industrial organization and other fields. Mixtures are also convenient in dealing with contaminated sampling models and models with multiple equilibria. This paper shows that finite mixture models are nonparametrically identified under weak assumptions that are plausible in economic applications. The key is to utilize the identification power implied by information in covariates variation. It then shows that fully nonparametric estimation of the entire mixture model is possible, by forming a sample analogue of one of the new identification strategies. The estimator is shown to possess a desirable polynomial rate of convergence. Lastly, an application to auction models with unobserved heterogeneity is discussed. This may be attractive for practitioners as it offers new ways to account for unobserved heterogeneity in auctions while allowing for arbitrary affiliation patterns and imposing only mild separability restrictions.

Contact Letty Diaz letty.diaz@caltech.edu at 626-395-1255
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