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GARTFIMA process and its empirical spectral density based estimation

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  • Niharika Bhootna
  • Arun Kumar

Abstract

In this article, we introduce a Gegenbauer autoregressive tempered fractionally integrated moving average process. We work on the spectral density and autocovariance function for the introduced process. The parameter estimation is done using the empirical spectral density with the help of the nonlinear least square technique and the Whittle likelihood estimation technique. The performance of the proposed estimation techniques is assessed on simulated data. Further, the introduced process is shown to better model the real-world data in comparison to other time series models.

Suggested Citation

  • Niharika Bhootna & Arun Kumar, 2024. "GARTFIMA process and its empirical spectral density based estimation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 51(10), pages 1919-1945, July.
    • Handle: RePEc:taf:japsta:v:51:y:2024:i:10:p:1919-1945
    DOI: 10.1080/02664763.2023.2249270
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