Autor(es): Gamerman, Dani
Data: 2014
Identificador Persistente: http://hdl.handle.net/10438/12213
Origem: Oasisbr
Assunto(s): Bayesian; Metropolis-Hastings algorithms; Reparametrization; Sampling schemes; System disturbances; Adjusted time series; Economia; Processo estocástico; Monte Carlo, Método de
This paper presents new methodology for making Bayesian inference about dy~ o!s for exponential famiIy observations. The approach is simulation-based _~t> use of ~vlarkov chain Monte Carlo techniques. A yletropolis-Hastings i:U~UnLlllll 1::; combined with the Gibbs sampler in repeated use of an adjusted version of normal dynamic linear models. Different alternative schemes are derived and compared. The approach is fully Bayesian in obtaining posterior samples for state parameters and unknown hyperparameters. Illustrations to real data sets with sparse counts and missing values are presented. Extensions to accommodate for general distributions for observations and disturbances. intervention. non-linear models and rnultivariate time series are outlined.
