Bayesian dynamic linear models (DLM) are useful in time series modelling because of the flexibility that they present in obtaining a good forecast. They are based on a decomposition of the relevant factors which explain the behavior of the series through a series of state parameters....

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...

This paper is concerned with the study of Bayesian inference procedures to commonly used time series models. In particular, the dynamic or state-space models, the time-varying vector autoregressive model and the structural vector autoregressive model are considered in detail. Inference...

Space-varying regression models are generalizations of standard linear models where the regression coefficients are allowed to change in space. The spatial structure is specified by a multivariate extension of pairwise difference pri- ors thus enabling incorporation of neighboring structures and...

In this paper, we analyze the productivity of farms across n = 370 municipalities located in the Center-West region of Brazil. We propose a stochastic frontier model with a latent spatial structure to account for possible unknown geographical variation of the outputs. This spatial component is...