Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models *

Abstract : Invertibility conditions for observation-driven time series models often fail to be guaranteed in empirical applications. As a result, the asymptotic theory of maximum likelihood and quasi-maximum likelihood estimators may be compromised. We derive considerably weaker conditions that can be used in practice to ensure the consistency of the maximum likelihood estimator for a wide class of observation-driven time series models. Our consistency results hold for both correctly specified and misspecified models. The practical relevance of the theory is highlighted in a set of empirical examples. We further obtain an asymptotic test and confidence bounds for the unfeasible " true " invertibility region of the parameter space.
Type de document :
Pré-publication, Document de travail
2016
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https://hal.archives-ouvertes.fr/hal-01377971
Contributeur : Olivier Wintenberger <>
Soumis le : samedi 8 octobre 2016 - 10:06:15
Dernière modification le : mercredi 28 novembre 2018 - 01:26:23
Document(s) archivé(s) le : lundi 9 janvier 2017 - 12:18:32

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  • HAL Id : hal-01377971, version 1
  • ARXIV : 1610.02863

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F Blasques, P Gorgi, S Koopman, O Wintenberger. Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models *. 2016. 〈hal-01377971〉

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