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A dynamic model for double‐bounded time series with chaotic‐driven conditional averages

Author

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  • Guilherme Pumi
  • Taiane Schaedler Prass
  • Rafael Rigão Souza
Abstract
In this work, we introduce a class of dynamic models for time series taking values on the unit interval. The proposed model follows a generalized linear model approach where the random component, conditioned on the past information, follows a beta distribution, while the conditional mean specification may include covariates and also an extra additive term given by the iteration of a map that can present chaotic behavior. The resulting model is very flexible and its systematic component can accommodate short‐ and long‐range dependence, periodic behavior, laminar phases, etc. We derive easily verifiable conditions for the stationarity of the proposed model, as well as conditions for the law of large numbers and a Birkhoff‐type theorem to hold. A Monte Carlo simulation study is performed to assess the finite sample behavior of the partial maximum likelihood approach for parameter estimation in the proposed model. Finally, an application to the proportion of stored hydroelectrical energy in Southern Brazil is presented.

Suggested Citation

  • Guilherme Pumi & Taiane Schaedler Prass & Rafael Rigão Souza, 2021. "A dynamic model for double‐bounded time series with chaotic‐driven conditional averages," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 68-86, March.
    • Handle: RePEc:bla:scjsta:v:48:y:2021:i:1:p:68-86
    DOI: 10.1111/sjos.12439
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    References listed on IDEAS

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    1. Hu, Tien-Chung & Rosalsky, Andrew & Volodin, Andrei, 2008. "On convergence properties of sums of dependent random variables under second moment and covariance restrictions," Statistics & Probability Letters, Elsevier, vol. 78(14), pages 1999-2005, October.
    2. Benjamin M.A. & Rigby R.A. & Stasinopoulos D.M., 2003. "Generalized Autoregressive Moving Average Models," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 214-223, January.
    3. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    4. Konstantinos Fokianos & Benjamin Kedem, 2004. "Partial Likelihood Inference For Time Series Following Generalized Linear Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(2), pages 173-197, March.
    5. Andréa Rocha & Francisco Cribari-Neto, 2009. "Beta autoregressive moving average models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(3), pages 529-545, November.
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    Cited by:

    1. Scher, Vinícius T. & Cribari-Neto, Francisco & Bayer, Fábio M., 2024. "Generalized βARMA model for double bounded time series forecasting," International Journal of Forecasting, Elsevier, vol. 40(2), pages 721-734.

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