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A Multinomial Probit Model with Latent Factors: Identification and Interpretation without a Measurement System

Rémi Piatek and Miriam Gensowski ()
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Miriam Gensowski: University of Copenhagen

No 11042, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: We develop a parametrization of the multinomial probit model that yields greater insight into the underlying decision-making process, by decomposing the error terms of the utilities into latent factors and noise. The latent factors are identified without a measurement system, and they can be meaningfully linked to an economic model. We provide sufficient conditions that make this structure identified and interpretable. For inference, we design a Markov chain Monte Carlo sampler based on marginal data augmentation. A simulation exercise shows the good numerical performance of our sampler and reveals the practical importance of alternative identification restrictions. Our approach can generally be applied to any setting where researchers can specify an a priori structure on a few drivers of unobserved heterogeneity. One such example is the choice of combinations of two options, which we explore with real data on education and occupation pairs.

Keywords: multinomial probit; latent factors; Bayesian analysis; marginal data augmentation; educational choice; occupational choice (search for similar items in EconPapers)
JEL-codes: C11 C25 C35 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2017-09
New Economics Papers: this item is included in nep-cta, nep-dcm, nep-ecm and nep-ore
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