An Exponential Autoregressive Time Series Model for Complex Data

Gholamreza Hesamian, Faezeh Torkian, Arne Johannssen () and Nataliya Chukhrova
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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

Mathematics, 2023, vol. 11, issue 19, 1-12

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.

Keywords: AR model; ARMA model; fuzzy nonlinear time series; fuzzy data; time series analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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