Maximum likelihood estimation for score-driven models
Janneke van Brummelen,
Siem Jan Koopman () and
Andre Lucas ()
Journal of Econometrics, 2022, vol. 227, issue 2, 325-346
We establish strong consistency and asymptotic normality of the maximum likelihood estimator for stochastic time-varying parameter models driven by the score of the predictive conditional likelihood function. For this purpose, we formulate primitive conditions for global identification, invertibility, strong consistency, and asymptotic normality both under correct specification and misspecification of the model. A detailed illustration is provided for a conditional volatility model with disturbances from the Student’s t distribution.
Keywords: Time-varying parameters; Markov processes; Stationarity; Invertibility; Consistency; Asymptotic normality (search for similar items in EconPapers)
JEL-codes: C13 C22 (search for similar items in EconPapers)
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Working Paper: Maximum Likelihood Estimation for Score-Driven Models (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:227:y:2022:i:2:p:325-346
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