Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/107786
Authors: 
Blasques, Francisco
Koopman, Siem Jan
Lucas, and André
Year of Publication: 
2014
Series/Report no.: 
Tinbergen Institute Discussion Paper 14-074/III
Abstract: 
The strong consistency and asymptotic normality of the maximum likelihood estimator in observation-driven models usually requires the study of the model both as a filter for the time-varying parameter and as a data generating process (DGP) for observed data. The probabilistic properties of the filter can be substantially different from those of the DGP. This difference is particularly relevant for recently developed time varying parameter models. We establish new conditions under which the dynamic properties of the true time varying parameter as well as of its filtered counterpart are both well-behaved and We only require the verification of one rather than two sets of conditions. In particular, we formulate conditions under which the (local) invertibility of the model follows directly from the stable behavior of the true time varying parameter. We use these results to prove the local strong consistency and asymptotic normality of the maximum likelihood estimator. To illustrate the results, we apply the theory to a number of empirically relevant models.
Subjects: 
Observation-driven models
stochastic recurrence equations
contraction conditions
invertibility
stationarity
ergodicity
generalized autoregressive score models
JEL: 
C13
C22
C12
Document Type: 
Working Paper

Files in This Item:
File
Size
533.57 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.