Estimating Multivariate Latent-Structure Models

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

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Abstract: A constructive proof of identification of multilinear decompositions of multiway arrays is presented. It can be applied to show identification in a variety of multivariate latent structures. Examples are finite-mixture models and hidden Markov models. The key step to show identification is the joint diagonalization of a set of matrices in the same non-orthogonal basis. An estimator of the latent-structure model may then be based on a sample version of this simultaneous-diagonalization problem. Simple algorithms are available for computation. Asymptotic theory is derived for this joint approximate-diagonalization estimator.

Keywords: hidden Markov model; finite mixture model; latent structure; multilinear restrictions; multivariate data; nonparametric estimation; simultaneous matrix diagonalization (search for similar items in EconPapers)
Date: 2014-12-01
Note: View the original document on HAL open archive server: https://sciencespo.hal.science/hal-01097135v2
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Related works:
Working Paper: Estimating Multivariate Latent-Structure Models (2016)
Working Paper: Estimating Multivariate Latent-Structure Models (2016)
Working Paper: Estimating Multivariate Latent-Structure Models (2014)
Working Paper: Estimating Multivariate Latent-Structure Models (2014)
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