Multivariate spatial regression models

Dani Gamerman and Ajax Moreira ()

Journal of Multivariate Analysis, 2004, vol. 91, issue 2, 262-281

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 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.

Keywords: Bayesian; Common; component; models; Gibbs; sampling; Hyperparameters; Markov; chain; Monte; Carlo; Metropolis-Hastings; algorithm (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (10)

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