Journal
COMPUTATIONAL STATISTICS
Volume 28, Issue 5, Pages 2267-2293Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s00180-013-0406-9
Keywords
Bayesian paradigm; Linear Bayes; Nonlinear models; Markov chain Monte Carlo
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Funding
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), Brazil
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), Brazil
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A multimove sampling scheme for the state parameters of non-Gaussian and nonlinear dynamic models for univariate time series is proposed. This procedure follows the Bayesian framework, within a Gibbs sampling algorithm with steps of the Metropolis-Hastings algorithm. This sampling scheme combines the conjugate updating approach for generalized dynamic linear models, with the backward sampling of the state parameters used in normal dynamic linear models. A quite extensive Monte Carlo study is conducted in order to compare the results obtained using our proposed method, conjugate updating backward sampling (CUBS), with those obtained using some algorithms previously proposed in the Bayesian literature. We compare the performance of CUBS with other sampling schemes using two real datasets. Then we apply our algorithm in a stochastic volatility model. CUBS significantly reduces the computing time needed to attain convergence of the chains, and is relatively simple to implement.
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