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Maximum likelihood estimation for score-driven models
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DOI：10.1016/j.jeconom.2021.06.003.png)
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Abstract
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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. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Time-varying parameters
Markov processes
Stationarity
Invertibility
Consistency
Asymptotic normality
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