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Erscheinungsjahr: 
2014
Schriftenreihe/Nr.: 
Tinbergen Institute Discussion Paper No. 14-029/III
Verlag: 
Tinbergen Institute, Amsterdam and Rotterdam
Zusammenfassung: 
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.
Schlagwörter: 
time-varying parameter models
GAS
score driven models
Markov processes estimation
stationarity
invertibility
consistency
asymptotic normality
JEL: 
C13
C22
C12
Dokumentart: 
Working Paper
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