Maximum Likelihood Estimation for Score-Driven Models

Francisco Blasques (), Siem Jan Koopman and Andre Lucas

No 14-029/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: We establish the strong consistency and asymptotic normality of the maximum likelihood estimator for time-varying parameter models driven by the score of the predictive likelihood function. We formulate primitive conditions for global identification, invertibility, strong consistency, and asymptotic normality under both correct specification and mis-specification of the model. A detailed illustration is provided for a conditional volatility model with disturbances from the Student's t distribution.

Keywords: score-driven models; time-varying parameters; Markov processes; stationarity; invertibility; consistency; asymptotic normality (search for similar items in EconPapers)
JEL-codes: C12 C13 C22 (search for similar items in EconPapers)
Date: 2014-03-04, Revised 2017-10-23
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

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Journal Article: Maximum likelihood estimation for score-driven models (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20140029

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