期刊
COMPUTATIONAL STATISTICS & DATA ANALYSIS
卷 74, 期 -, 页码 64-80出版社
ELSEVIER
DOI: 10.1016/j.csda.2013.12.008
关键词
Dynamic models; Skew-normal distribution; Bayesian analysis; Kalman filter; Gibbs sampler
资金
- Improve University Research (CAPES-Brazil) [PROCAD-NF 2008]
- CNPq-Brazil, BPPesq
- National Research Council (CNPq-Brazil, BPPesq)
- Rio de Janeiro State Research Foundation (Faperj)
- CAPES via the Pronex project
- CNPq (via Projects Universal and CT-Amazonia)
- CAPES (via Project PROCAD 2007)
We develop a Bayesian dynamic model for modeling and forecasting multivariate time series relaxing the assumption of normality for the initial distribution of the state space parameter, and replacing it by a more flexible class of distributions, which we call Generalized Skew-Normal (GSN) Distributions. We develop a version of the classic Kalman filter, again obtaining GSN predictive and filtering distributions. As we are supposing the random fluctuations covariances to be unknown, a Gibbs-type sampler algorithm is developed in order to perform Bayesian inference. We work with two simulation experiments with scenarios close to real problems in order to show the efficacy of our proposed model. Finally, we apply our technique to a real data set. (C) 2014 Elsevier B.V. All rights reserved.
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