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Bayesian analysis of a dynamic multivariate spatial ordered probit model

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  • Ping Gao
  • Zixiang Lu

Abstract

Spatial econometrics has few studies on multivariate ordinal responses. This study proposes a dynamic multivariate spatial ordered probit (DMSOP) model, which is the first attempt to capture temporal and spatial dependencies simultaneously for multivariate ordinal responses. The parameters are calculated using Bayesian inference based on Markov chain Monte Carlo sampling. The DMSOP model performs effectively with the simulated data. Furthermore, the DMSOP model is applied to two response variables, namely, the life satisfaction and self-rated health of adults in 25 provinces in China. The empirical results show that the model can effectively measure the spatial and temporal dependencies for multivariate ordinal responses.

Suggested Citation

  • Ping Gao & Zixiang Lu, 2023. "Bayesian analysis of a dynamic multivariate spatial ordered probit model," Spatial Economic Analysis, Taylor & Francis Journals, vol. 18(4), pages 462-485, October.
    • Handle: RePEc:taf:specan:v:18:y:2023:i:4:p:462-485
    DOI: 10.1080/17421772.2023.2181384
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