Overview of Identification Methods of Autoregressive Model in Presence of Additive Noise

Dmitriy Ivanov () and Zaineb Yakoub
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Dmitriy Ivanov: Department of Information Systems Security, Samara National Research University, 443086 Samara, Russia
Zaineb Yakoub: Department of Electrical Engineering, National Engineering School of Gabes, University of Gabes, Gabes 6029, Tunisia

Mathematics, 2023, vol. 11, issue 3, 1-21

Abstract: This paper presents an overview of the main methods used to identify autoregressive models with additive noises. The classification of identification methods is given. For each group of methods, advantages and disadvantages are indicated. The article presents the simulation results of a large number of the described methods and gives recommendations on choosing the best methods.

Keywords: autoregressive model; additive noise; Yule-Walker equations; bias-compensated least squares; Frisch scheme; total least squares; errors-in-variables; prediction error method; maximum likelihood (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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