Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models
Statistical Finance
2016-10-11 v1 Statistics Theory
Statistics Theory
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
Cite
@article{arxiv.1610.02863,
title = {Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models},
author = {F Blasques and P Gorgi and S Koopman and O Wintenberger},
journal= {arXiv preprint arXiv:1610.02863},
year = {2016}
}