Multi-dimensional multivariate Gaussian Markov random fields with application to image processing
Journal of Multivariate Analysis
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Space-varying regression models: specifications and simulation
Computational Statistics & Data Analysis - Special issue: Computational econometrics
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This paper describes the inference procedures required to perform Bayesian inference to some multivariate econometric models. These models have a spatial component built into commonly used multivariate models. In particular, the common component models are addressed and extended to accommodate for spatial dependence. Inference procedures are based on a variety of simulation-based schemes designed to obtain samples from the posterior distribution of model parameters. They are also used to provide a basis to forecast new observations.