Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/98908
Authors: 
Blasques, Francisco
Koopman, Siem Jan
Lucas, Andre
Year of Publication: 
2014
Series/Report no.: 
Tinbergen Institute Discussion Paper 14-029/III
Abstract: 
We study the strong consistency and asymptotic normality of the maximum likelihood estimator for a class of time series models driven by the score function of the predictive likelihood. This class of nonlinear dynamic models includes both new and existing observation driven time series models. Examples include models for generalized autoregressive conditional heteroskedasticity, mixed-measurement dynamic factors, serial dependence in heavy-tailed densities, and other time varying parameter processes. We formulate primitive conditions for global identification, invertibility, strong consistency, asymptotic normality under correct specification and under mis-specification. We provide key illustrations of how the theory can be applied to specific dynamic models.
Subjects: 
time-varying parameter models
GAS
score driven models
Markov processes estimation
stationarity
invertibility
consistency
asymptotic normality
JEL: 
C13
C22
C12
Document Type: 
Working Paper

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