Harvey, A. C.

Score-Driven Time Series Models

Annual Review of Statistics and Its Application

Vol. 9 pp. 321-342 (2022)

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.

Keywords: copula, count data, directional data, generalized autoregressive conditional heteroscedasticity, generalized beta distribution of the second kind, observation-driven model, robustness

Author links: Andrew Harvey  

Publisher's Link: https://doi.org/10.1146/annurev-statistics-040120-021023