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

Francisco Blasques, Paolo Gorgi, Siem Jan Koopman and Olivier Wintenberger
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Francisco Blasques: Tinbergen Institute, Rotterdam, Department of Econometrics [Amsterdam] - UvA - University of Amsterdam [Amsterdam] = Universiteit van Amsterdam
Paolo Gorgi: Unipd - Università degli Studi di Padova = University of Padua, Department of Econometrics [Amsterdam] - UvA - University of Amsterdam [Amsterdam] = Universiteit van Amsterdam

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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)
Date: 2018-01-01
Note: View the original document on HAL open archive server: https://hal.science/hal-01377971v1
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Citations: View citations in EconPapers (2)

Published in Electronic Journal of Statistics , 2018, 12 (1), ⟨10.1214/18-EJS1416⟩

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Related works:
Working Paper: Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01377971

DOI: 10.1214/18-EJS1416

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