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Asymptotic behavior of the Laplacian quasi-maximum likelihood estimator of affine causal processes

By Jean-Marc Bardet

Appears in collection : Thematic month on statistics - Week 3: Processus / Mois thématique sur les statistiques - Semaine 3 : Processus

We prove the consistency and asymptotic normality of the Laplacian Quasi-Maximum Likelihood Estimator (QMLE) for a general class of causal time series including ARMA, AR($\infty$), GARCH, ARCH($\infty$), ARMA-GARCH, APARCH, ARMA-APARCH,..., processes. We notably exhibit the advantages (moment order and robustness) of this estimator compared to the classical Gaussian QMLE. Numerical simulations confirms the accuracy of this estimator.

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Citation data

  • DOI 10.24350/CIRM.V.18931403
  • Cite this video Bardet, Jean-Marc (16/02/2016). Asymptotic behavior of the Laplacian quasi-maximum likelihood estimator of affine causal processes. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.18931403
  • URL https://dx.doi.org/10.24350/CIRM.V.18931403

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Bibliography

  • [1] Bardet, J.-M., Boularouk, Y., Djaballah, K. (2016). Asymptotic behavior of the Laplacian quasi-maximum likelihood estimator of affine causal processes. <arXiv:1601.00155> - http://arxiv.org/abs/1601.00155
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