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Article Dans Une Revue Electronic Journal of Statistics Année : 2018

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

Résumé

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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Dates et versions

hal-01377971 , version 1 (08-10-2016)

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Francisco Blasques, Paolo Gorgi, Siem Jan Koopman, Olivier Wintenberger. Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models. Electronic Journal of Statistics , 2018, 12 (1), ⟨10.1214/18-EJS1416⟩. ⟨hal-01377971⟩
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