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Dynamic Bayesian beta models

  • da-Silva, C.Q.
  • Migon, H.S.
  • Correia, L.T.
Registered author(s):

    We develop a dynamic Bayesian beta model for modeling and forecasting single time series of rates or proportions. This work is related to a class of dynamic generalized linear models (DGLMs), although, for convenience, we use non-conjugate priors. The proposed methodology is based on approximate analysis relying on Bayesian linear estimation, nonlinear system of equations solution and Gaussian quadrature. Intentionally we avoid MCMC strategy, keeping the desired sequential nature of the Bayesian analysis. Applications to both real and simulated data are provided.

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    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 55 (2011)
    Issue (Month): 6 (June)
    Pages: 2074-2089

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    Handle: RePEc:eee:csdana:v:55:y:2011:i:6:p:2074-2089
    Contact details of provider: Web page: http://www.elsevier.com/locate/csda

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    1. Max Bruche & Carlos Gonzalez-Aguado, 2006. "Recovery rates, default probabilities and the credit cycle," LSE Research Online Documents on Economics 24524, London School of Economics and Political Science, LSE Library.
    2. 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.
    3. H�vard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392.
    4. Ed McKenzie, 1985. "An Autoregressive Process for Beta Random Variables," Management Science, INFORMS, vol. 31(8), pages 988-997, August.
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