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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.
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Preprints, Working Papers, ...
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https://hal.archives-ouvertes.fr/hal-01377971
Contributor : Olivier Wintenberger <>
Submitted on : Saturday, October 8, 2016 - 10:06:15 AM
Last modification on : Monday, January 20, 2020 - 2:02:03 PM
Document(s) archivé(s) le : Monday, January 9, 2017 - 12:18:32 PM

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

Citation

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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