Blasques, Francisco Gorgi, Paolo Koopman, Siem Jan Wintenberger, Olivier
Year of Publication:
Tinbergen Institute Discussion Paper 16-082/III
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.
consistency invertibility maximum likelihood estimation observation-driven models stochastic recurrence equations