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Functional autoregressive process with seasonality

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  • Fatna Bensaber
  • Tahar Mourid

Abstract

We study the estimation of a seasonality perturbed by a continuous time process admitting a C[0,δ]-valued autoregressive representation where C[0,δ] is the Banach space of continuous functions on [0,δ], δ>0. We provide the almost sure convergence, asymptotic normality and compact iterated logarithm law. Following Antoniadis A. (1982) we construct in the framework of functional autoregressive processes (non i.i.d. case), confidence balls for the seasonality in the space C[0,δ] from compact iterated logarithm law. Then when seasonality belongs to a finite dimensional space (dimensional reduction), we study the seasonality estimation giving its asymptotic properties. Finally, we examine an estimator of the dimension of this space when it is unknown. Numerical simulations illustrate the asymptotic results of the estimators.

Suggested Citation

  • Fatna Bensaber & Tahar Mourid, 2023. "Functional autoregressive process with seasonality," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(20), pages 7131-7145, October.
    • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:20:p:7131-7145
    DOI: 10.1080/03610926.2022.2093907
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