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A dynamic linear model with extended skew-normal for the initial distribution of the state parameter

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  • Cabral, Celso Rômulo Barbosa
  • da-Silva, Cibele Queiroz
  • Migon, Helio S.

Abstract

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.

Suggested Citation

  • Cabral, Celso Rômulo Barbosa & da-Silva, Cibele Queiroz & Migon, Helio S., 2014. "A dynamic linear model with extended skew-normal for the initial distribution of the state parameter," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 64-80.
  • Handle: RePEc:eee:csdana:v:74:y:2014:i:c:p:64-80
    DOI: 10.1016/j.csda.2013.12.008
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    References listed on IDEAS

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    1. Adelchi Azzalini, 2005. "The Skew‐normal Distribution and Related Multivariate Families," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(2), pages 159-188, June.
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    3. Nakajima, Jouchi & Omori, Yasuhiro, 2012. "Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student’s t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3690-3704.
    4. Arellano-Valle, Reinaldo B. & Genton, Marc G., 2005. "On fundamental skew distributions," Journal of Multivariate Analysis, Elsevier, vol. 96(1), pages 93-116, September.
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    8. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    12. Grzegorz Grabek & Bohdan Klos & Grzegorz Koloch, 2011. "Skew-normal shocks in the linear state space form DSGE model," NBP Working Papers 101, Narodowy Bank Polski.
    13. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389, May.
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    Cited by:

    1. Gaygysyz Guljanov & Willi Mutschler & Mark Trede, 2022. "Pruned Skewed Kalman Filter and Smoother: With Application to the Yield Curve," CQE Working Papers 10122, Center for Quantitative Economics (CQE), University of Muenster.
    2. Reinaldo B. Arellano-Valle & Javier E. Contreras-Reyes & Freddy O. López Quintero & Abel Valdebenito, 2019. "A skew-normal dynamic linear model and Bayesian forecasting," Computational Statistics, Springer, vol. 34(3), pages 1055-1085, September.

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