Quasi-Maximum Likelihood Estimation of long-memory linear processes
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916256469221376 |
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| author | Bardet, Jean-Marc Mbienkeu, Yves Gael Tchabo |
| author_facet | Bardet, Jean-Marc Mbienkeu, Yves Gael Tchabo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_14711 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Quasi-Maximum Likelihood Estimation of long-memory linear processes Bardet, Jean-Marc Mbienkeu, Yves Gael Tchabo Statistics Theory 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. |
| title | Quasi-Maximum Likelihood Estimation of long-memory linear processes |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2310.14711 |