Application of the Periodic Self-Exciting Threshold Autoregressive Model

Authors

  • Nesrine Bezziche UBMA
  • Mouna Merzougui UBMA

DOI:

https://doi.org/10.19139/soic-2310-5070-2254

Keywords:

Periodic Self-Exciting Threshold Autoregressive models, LS estimation, LR test, LAN property, Algeria's temperature.

Abstract

In this paper, we analyze Algerian temperature data using the periodic self-exciting threshold autoregressive (PSETAR) model. Despite the significant advantages offered by the periodic SETAR model in capturing seasonal and threshold-based behaviors, it remains underutilized in practical applications. The goal of this work is to demonstrate the utility of this model by applying it to Algeria's temperature series. We examine the properties of the model and discuss its estimation using the least squares method. The linearity is tested using the likelihood ratio test, and we extend the local asymptotic normality to p regimes. This analysis provides a deeper understanding of the temperature dynamics in Algeria, highlighting the model's ability to capture seasonal variations and thresholds.

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Published

2025-01-29

Issue

Vol. 13 No. 3 (2025)

Section

Research Articles

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How to Cite

Application of the Periodic Self-Exciting Threshold Autoregressive Model. (2025). Statistics, Optimization & Information Computing, 13(3), 1339-1356. https://doi.org/10.19139/soic-2310-5070-2254