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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407621001743
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Maximum Likelihood Estimation for Score-Driven Models (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:227:y:2022:i:2:p:325-346

DOI: 10.1016/j.jeconom.2021.06.003

Access Statistics for this article

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

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().