An efficient sampling scheme for dynamic generalized models

Helio Migon (), Alexandra Schmidt, Romy Ravines and João Pereira

Computational Statistics, 2013, vol. 28, issue 5, 2267-2293

Abstract: 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. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Bayesian paradigm; Linear Bayes; Nonlinear models; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:28:y:2013:i:5:p:2267-2293

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DOI: 10.1007/s00180-013-0406-9

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