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

Francisco Blasques (), Paolo Gorgi, Siem Jan Koopman and Olivier Wintenberger
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Paolo Gorgi: VU University Amsterdam, the Netherlands; University of Padua, Italy

No 16-082/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

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.

Keywords: consistency; invertibility; maximum likelihood estimation; observation-driven models; stochastic recurrence equations (search for similar items in EconPapers)
JEL-codes: C13 C32 C58 (search for similar items in EconPapers)
Date: 2016-10-06
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (18)

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