Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2016-10-06
Number: 16-082/III
Author-Name: Francisco Blasques
Author-Workplace-Name: VU University Amsterdam, the Netherlands
Author-Name: Paolo Gorgi
Author-Workplace-Name: VU University Amsterdam, the Netherlands; University of Padua, Italy
Author-Name: Siem Jan Koopman
Author-Workplace-Name: VU University Amsterdam, the Netherlands; Aarhus University, Denmark
Author-Name: Olivier Wintenberger
Author-Workplace-Name: University of Copenhagen, Denmark; Sorbonne Universités, UPMC University Paris 06, France
Title: Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models
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.
Classification-JEL: C13, C32, C58
Keywords: consistency, invertibility, maximum likelihood estimation, observation-driven models, stochastic recurrence equations
File-Url: https://papers.tinbergen.nl/16082.pdf
File-Format: application/pdf
File-Size: 483177 bytes
Handle: RePEc:tin:wpaper:20160082