Multivariate spatial regression models
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.
Year of publication: |
2004
|
---|---|
Authors: | Gamerman, Dani ; Moreira, Ajax R. B. |
Published in: |
Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 91.2004, 2, p. 262-281
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Publisher: |
Elsevier |
Keywords: | Bayesian Common component models Gibbs sampling Hyperparameters Markov chain Monte Carlo Metropolis-Hastings algorithm |
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