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Parameter estimation for fractional autoregressive process with seasonal structure

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  • Chunhao Cai
  • Yiwu Shang

Abstract

This paper introduces a new kind of seasonal fractional autoregressive process (SFAR) driven by fractional Gaussian noise (fGn). The new model includes a standard seasonal AR model and fGn. The estimation of the parameters of this new model has to solve two problems: nonstationarity from the seasonal structure and long memory from fGn. We innovatively solve these by getting a stationary subsequence, making a stationary additive sequence and then obtaining their spectral density. Then we use one-step procedure for Generalized Least Squares Estimator (GLSE) and the Geweke Porter–Hudak (GPH) method to get better results. We prove that both the initial and one-step estimators are consistent and asymptotically normal. Finally, we use Monte Carlo simulations with finite-sized samples to demonstrate the performance of these estimators. Moreover, through empirical analysis, it is shown that the SFAR model can simulate some real-world phenomena better than general models.

Suggested Citation

  • Chunhao Cai & Yiwu Shang, 2025. "Parameter estimation for fractional autoregressive process with seasonal structure," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 9(3), pages 255-284, July.
    • Handle: RePEc:taf:tstfxx:v:9:y:2025:i:3:p:255-284
    DOI: 10.1080/24754269.2025.2537487
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