Harvey, A. C.

Score-Driven Time Series Models

Annual Review of Statistics and Its Application, to appear

(2021)

Abstract: The construction of score-driven filters for nonlinear time series models is described and it is shown how they 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

JEL Codes: C22, C32

Author links: Andrew Harvey  

Cambridge Working Paper in Economics Version of Paper: Score-Driven Time Series Models, Harvey, A., (2021)