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Positive time series regression models: theoretical and computational aspects

Author

Listed:
  • Taiane Schaedler Prass

    (Universidade Federal do Rio Grande do Sul)

  • Guilherme Pumi

    (Universidade Federal do Rio Grande do Sul)

  • Cleiton Guollo Taufemback

    (Universidade Federal do Rio Grande do Sul)

  • Jonas Hendler Carlos

    (Universidade Federal do Rio Grande do Sul)

Abstract

This paper discusses dynamic ARMA-type regression models for positive time series, which can handle bounded non-Gaussian time series without requiring data transformations. Our proposed model includes a conditional mean modeled by a dynamic structure containing autoregressive and moving average terms, time-varying covariates, unknown parameters, and link functions. Additionally, we present the PTSR package and discuss partial maximum likelihood estimation, asymptotic theory, hypothesis testing inference, diagnostic analysis, and forecasting for a variety of regression-based dynamic models for positive time series. A Monte Carlo simulation and a real data application are provided.

Suggested Citation

  • Taiane Schaedler Prass & Guilherme Pumi & Cleiton Guollo Taufemback & Jonas Hendler Carlos, 2025. "Positive time series regression models: theoretical and computational aspects," Computational Statistics, Springer, vol. 40(3), pages 1185-1215, March.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:3:d:10.1007_s00180-024-01531-z
    DOI: 10.1007/s00180-024-01531-z
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    References listed on IDEAS

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    1. Víctor Leiva & Helton Saulo & Rubens Souza & Robert G. Aykroyd & Roberto Vila, 2021. "A new BISARMA time series model for forecasting mortality using weather and particulate matter data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 346-364, March.
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