Bayesian analysis of switching regression models
type: Item
instance of: scholarly article
title: Bayesian analysis of switching regression models
zbMATH Open document ID: 0591.62096
DOI: 10.1016/0304-4076(85)90033-8
author: Michel Lubrano
published in: Journal of Econometrics
publication date: 1985-01-01T00:00:00Z
review text: A class of switching linear regression models of the form: \[ y_ t=\min (x'\!_{1t}\beta_ 1,x'\!_{2t}\beta_ 2)+u_ t,\quad u_ t\sim N(0,\sigma^ 2), \] is considered, where the change of two or more regimes is represented by the min-operator. Such models are relevant for disequilibrium market models in econometrics. Classical methods of maximum likelihood are compared with the Bayesian methods for deriving the posterior density of the parameters \((\beta,\sigma^ 2)\). A Monte Carlo procedure is proposed to compute the posterior moments and the numerical results confirm that the Bayesian estimator has a smaller mean square error than the maximum likelihood estimator.
Mathematics Subject Classification ID: 62P20
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 91B50
zbMATH DE Number: 3949561
zbMATH Keywords: change of regimes
zbMATH Keywords: local identification
zbMATH Keywords: switching linear regression models
zbMATH Keywords: min-operator
zbMATH Keywords: disequilibrium market models
zbMATH Keywords: posterior density
zbMATH Keywords: Monte Carlo procedure
zbMATH Keywords: posterior moments
zbMATH Keywords: mean square error
zbMATH Keywords: maximum likelihood estimator
MaRDI profile type: Publication
full work available at URL: 0304-4076(85)90033-8
OpenAlex ID: W2075133806
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description: scientific article; zbMATH DE number 3949561