Bayesian analysis of a multivariate spatial ordered probit model

Many phenomena are correlated and spatially dependent. In addition, some of them ordinal discrete responses. Thus, a model is needed to capture the interactions multivariate outcomes spatial dependences. Following Smith LeSage (2004) Jeliazkov et al. (2008), this study proposes new algorithm for ordered probit (MSOP) address need. applying model, parameters calculated using Bayesian inference based on Markov chain Monte Carlo (MCMC) sampling. The validity accuracy MSOP verified by simulated datasets, performs very well with data. illustrates it two response variables, self-rated health, life satisfaction elderly people in 18 representative provinces mainland China. empirical results show that dependences indispensable variables.