Bayesian analysis of multivariate ordered probit model with individual heterogeneity

Lei Shi ()
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Lei Shi: Hokkaido University

AStA Advances in Statistical Analysis, 2020, vol. 104, issue 4, No 5, 649-665

Abstract: Abstract In recent years, models incorporating heterogeneity among individuals have become increasingly popular in the analyses on subjective ordered choice data. However, there are rare previous studies that include individual heterogeneity in the multivariate ordered probit model. In this article, we describe the Bayesian multivariate ordered probit model introduced by Chen and Dey (in: Dey, Ghosh, Mallick (eds) Generalized linear models: a Bayesian perspective. Marcel-Dekker, New York, pp 133–157, 2000) (Algorithm 1), and propose a new algorithm that includes individual heterogeneity in the cutpoint function (Algorithm 2). Further, we examine the two algorithms using real data from World Values Survey wave 5, collected between 2005 and 2009. The empirical results demonstrate that the model with individual heterogeneity outperforms that without heterogeneity.

Keywords: Bayesian analysis; Markov chain Monte Carlo (MCMC); Multivariate ordered probit model; Individual heterogeneity; World values survey (search for similar items in EconPapers)
JEL-codes: C11 I31 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:alstar:v:104:y:2020:i:4:d:10.1007_s10182-020-00369-2

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DOI: 10.1007/s10182-020-00369-2

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