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An Exponential Autoregressive Time Series Model for Complex Data

Author

Listed:
  • Gholamreza Hesamian

    (Department of Statistics, Payame Noor University, Tehran 19395-3697, Iran)

  • Faezeh Torkian

    (Department of Statistics, Payame Noor University, Tehran 19395-3697, Iran)

  • Arne Johannssen

    (Faculty of Business Administration, University of Hamburg, 20146 Hamburg, Germany)

  • Nataliya Chukhrova

    (Faculty of Business Administration, University of Hamburg, 20146 Hamburg, Germany)

Abstract

In this paper, an exponential autoregressive model for complex time series data is presented. As for estimating the parameters of this nonlinear model, a three-step procedure based on quantile methods is proposed. This quantile-based estimation technique has the benefit of being more robust compared to least/absolute squares. The performance of the introduced exponential autoregressive model is evaluated by means of four established goodness-of-fit criteria. The practical utility of the novel time series model is showcased through a comparative analysis involving simulation studies and real-world data illustrations.

Suggested Citation

  • Gholamreza Hesamian & Faezeh Torkian & Arne Johannssen & Nataliya Chukhrova, 2023. "An Exponential Autoregressive Time Series Model for Complex Data," Mathematics, MDPI, vol. 11(19), pages 1-12, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4022-:d:1245194
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    References listed on IDEAS

    as
    1. Gholamreza Hesamian & Arne Johannssen & Nataliya Chukhrova, 2023. "A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data," Mathematics, MDPI, vol. 11(13), pages 1-17, June.
    2. Coppi, Renato & D'Urso, Pierpaolo & Giordani, Paolo & Santoro, Adriana, 2006. "Least squares estimation of a linear regression model with LR fuzzy response," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 267-286, November.
    3. Chao Wang & Heng Liu & Jian-Feng Yao & Richard A. Davis & Wai Keung Li, 2014. "Self-Excited Threshold Poisson Autoregression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 777-787, June.
    4. Torbat, Sheida & Khashei, Mehdi & Bijari, Mehdi, 2018. "A hybrid probabilistic fuzzy ARIMA model for consumption forecasting in commodity markets," Economic Analysis and Policy, Elsevier, vol. 58(C), pages 22-31.
    5. Granger, Clive W. J. & Terasvirta, Timo, 1993. "Modelling Non-Linear Economic Relationships," OUP Catalogue, Oxford University Press, number 9780198773207.
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