Journal
JOURNAL OF ECONOMETRICS
Volume 227, Issue 2, Pages 325-346Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2021.06.003
Keywords
Time-varying parameters; Markov processes; Stationarity; Invertibility; Consistency; Asymptotic normality
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Funding
- Dutch Research Council (NWO) [VICI453-09-005, VI.Vidi.195.099]
- CREATES, Aarhus University, Denmark - Danish National Research Foundation [DNRF78]
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The article discusses the maximum likelihood estimator for stochastic time-varying parameter models, focusing on global identification, invertibility, strong consistency, and asymptotic normality of the model. A detailed illustration is provided for a conditional volatility model.
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.
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