Quasi-maximum likelihood estimation of long-memory linear processes

Jean-Marc Bardet () and Yves Gael Tchabo MBienkeu ()
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Jean-Marc Bardet: University Panthéon-Sorbonne
Yves Gael Tchabo MBienkeu: University Panthéon-Sorbonne

Statistical Inference for Stochastic Processes, 2024, vol. 27, issue 3, No 1, 457-483

Abstract: Abstract The purpose of this paper is to study the convergence of the quasi-maximum likelihood (QML) estimator for long memory linear processes. We first establish a correspondence between the long-memory linear process representation and the long-memory AR $$(\infty )$$ ( ∞ ) process representation. We then establish the almost sure consistency and asymptotic normality of the QML estimator. Numerical simulations illustrate the theoretical results and confirm the good performance of the estimator.

Keywords: Long memory process; Semiparametric estimation; Linear process; Limit theorems (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11203-024-09313-6

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