Affiliation:
1. Faculty of Economics, Cambridge University, Cambridge CB3 9DD, United Kingdom;
Abstract
The construction of score-driven filters for nonlinear time series models is described, and they are shown to apply over a wide range of disciplines. Their theoretical and practical advantages over other methods are highlighted. Topics covered include robust time series modeling, conditional heteroscedasticity, count data, dynamic correlation and association, censoring, circular data, and switching regimes. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Cited by
8 articles.
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1. Hybrid Distribution Separation for Prediction of Heterogeneous Zero-Inflated Time Series;2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom);2023-12-21
2. Score-driven models for realized volatility;Journal of Econometrics;2023-12
3. Cryptocurrency volatility forecasting using commonality in intraday volatility;4th ACM International Conference on AI in Finance;2023-11-25
4. Jointly Forecasting Value-at-Risk and Expected Shortfall with Score-Driven Dynamic Relationships;2023
5. Generalized Autoregressive Score Trees and Forests;SSRN Electronic Journal;2023