A Bayesian Factor Model for Spatial Panel Data with a Separable Covariance Approach

A hierarchical Bayesian factor model for multivariate spatially and temporally correlated data is proposed. This method searches scores incorporating a dependence within observations due to both geographical temporal structure it an extension of proposed by Mezzetti (2012) using the results separable covariance matrix spatial panel as in Leorato (2016). Gibbs sampling algorithm implemented sample from posterior distributions. We illustrate benefit performance our analyzing death rates different diseases together with some socio-economical behavioural indicators simulated data.