Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/149486
Authors: 
Blasques, Francisco
Gorgi, Paolo
Koopman, Siem Jan
Wintenberger, Olivier
Year of Publication: 
2016
Series/Report no.: 
Tinbergen Institute Discussion Paper 16-082/III
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.
Subjects: 
consistency
invertibility
maximum likelihood estimation
observation-driven models
stochastic recurrence equations
JEL: 
C13
C32
C58
Document Type: 
Working Paper

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