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

Francisco Blasques (), P Gorgi, Siem Jan Koopman and Olivier Wintenberger
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P Gorgi: CREATES

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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.

Date: 2016-10
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Citations: View citations in EconPapers (7)

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Working Paper: Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models (2018)
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