Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/220205 
Year of Publication: 
2015
Series/Report no.: 
Discussion Paper No. 116
Publisher: 
Institute for Applied Economic Research (ipea), Brasília
Abstract: 
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 seemingly unrelated regression and vector autoregressive 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.
Subjects: 
Bayesian
Gibbs sampling
Hyperparameters
Markov chain Monte Carlo
Metropolis-Hastings algorithm
Vector autoregressive models
Document Type: 
Working Paper

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