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Maximum likelihood estimation for score-driven models

Francisco Blasques, Janneke van Brummelen, Siem Jan Koopman () and Andre Lucas ()

Journal of Econometrics, 2022, vol. 227, issue 2, 325-346

Abstract: 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)
Date: 2022
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Working Paper: Maximum Likelihood Estimation for Score-Driven Models (2017) Downloads
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DOI: 10.1016/j.jeconom.2021.06.003

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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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