Nonparametric estimation of non-exchangeable latent-variable models

Stéphane Bonhomme, Koen Jochmans and Jean-Marc Robin
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Stéphane Bonhomme: University of Chicago

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Abstract: We propose a two-step method to nonparametrically estimate multivariate models in which the observed outcomes are independent conditional on a discrete latent variable. Applications include microeconometric models with unobserved types of agents, regime-switching models, and models with misclassification error. In the first step, we estimate weights that transform moments of the marginal distribution of the data into moments of the conditional distribution of the data for given values of the latent variable. In the second step, these conditional moments are estimated as weighted sample averages. We illustrate the method by estimating a model of wages with unobserved heterogeneity on PSID data.

Keywords: Latent variable models; Unobserved heterogeneity; Finite mixtures; Hidden Markov models; Nonparametric estimation; Panel data; Wage dynamics (search for similar items in EconPapers)
Date: 2017-12
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Citations: View citations in EconPapers (6)

Published in Journal of Econometrics, 2017, 201 (2), pp.237 - 248. ⟨10.1016/j.jeconom.2017.08.006⟩

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Related works:
Journal Article: Nonparametric estimation of non-exchangeable latent-variable models (2017)
Working Paper: Nonparametric estimation of non-exchangeable latent-variable models (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03264006

DOI: 10.1016/j.jeconom.2017.08.006

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