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A Non-Gaussian Family Of State-Space Models With Exact Marginal Likelihood

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  • Dani Gamerman
  • Thiago Rezende Santos
  • Glaura C. Franco

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  • Dani Gamerman & Thiago Rezende Santos & Glaura C. Franco, 2013. "A Non-Gaussian Family Of State-Space Models With Exact Marginal Likelihood," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(6), pages 625-645, November.
    • Handle: RePEc:bla:jtsera:v:34:y:2013:i:6:p:625-645
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    1. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 361-393.
    2. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
    3. Harvey, Andrew C & Fernandes, C, 1989. "Time Series Models for Count or Qualitative Observations," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(4), pages 407-417, October.
    4. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models: Comments: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 413-417, October.
    5. Monica Chiogna and Carlo Gaetan & Carlo Gaetan, 2002. "Dynamic generalized linear models with application to environmental epidemiology," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(4), pages 453-468, October.
    6. Shephard, Neil, 1994. "Local scale models : State space alternative to integrated GARCH processes," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 181-202.
    7. Godolphin, E.J. & Triantafyllopoulos, Kostas, 2006. "Decomposition of time series models in state-space form," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2232-2246, May.
    8. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    9. Cavanaugh, Joseph E. & Shumway, Robert H., 1996. "On computing the expected Fisher information matrix for state-space model parameters," Statistics & Probability Letters, Elsevier, vol. 26(4), pages 347-355, March.
    10. Harvey, Andrew C & Fernandes, C, 1989. "Time Series Models for Count or Qualitative Observations: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(4), pages 422-422, October.
    11. Dethlefsen, Claus & Lundbye-Christensen, Søren, 2006. "Formulating State Space Models in R with Focus on Longitudinal Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 16(i01).
    12. 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.
    13. Ole E. Barndorff‐Nielsen & Neil Shephard, 2001. "Non‐Gaussian Ornstein–Uhlenbeck‐based models and some of their uses in financial economics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 167-241.
    14. Christophe Andrieu & Arnaud Doucet, 2002. "Particle filtering for partially observed Gaussian state space models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 827-836, October.
    15. Unknown, 1986. "Letters," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 1(4), pages 1-9.
    16. Dani Gamerman, 1991. "Dynamic Bayesian Models for Survival Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 40(1), pages 63-79, March.
    17. Pitt, Michael K. & Walker, Stephen G., 2005. "Constructing Stationary Time Series Models Using Auxiliary Variables With Applications," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 554-564, June.
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    Cited by:

    1. de Pinho, Frank M. & Franco, Glaura C. & Silva, Ralph S., 2016. "Modeling volatility using state space models with heavy tailed distributions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 119(C), pages 108-127.
    2. Duca, Victor E.L.A. & Fonseca, Thaís C.O. & Cyrino Oliveira, Fernando L., 2021. "A generalized dynamical model for wind speed forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    3. Payam Piray & Nathaniel D. Daw, 2021. "A model for learning based on the joint estimation of stochasticity and volatility," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    4. Duca, Victor E.L.A. & Fonseca, Thaís C.O. & Cyrino Oliveira, Fernando Luiz, 2023. "An overview of non-Gaussian state-space models for wind speed data," Energy, Elsevier, vol. 266(C).
    5. Magda Monteiro & Marco Costa, 2023. "Change Point Detection by State Space Modeling of Long-Term Air Temperature Series in Europe," Stats, MDPI, vol. 6(1), pages 1-18, January.
    6. Arthur T. Rego & Thiago R. dos Santos, 2018. "Non-Gaussian Stochastic Volatility Model with Jumps via Gibbs Sampler," Papers 1809.01501, arXiv.org, revised Oct 2018.
    7. T. R. Santos, 2018. "A Bayesian GED-Gamma stochastic volatility model for return data: a marginal likelihood approach," Papers 1809.01489, arXiv.org.
    8. Payam Piray & Nathaniel D Daw, 2020. "A simple model for learning in volatile environments," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-26, July.

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