Score-Driven Time Series Models,Annual Review of Statistics and Its Application

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Score-Driven Time Series Models
Annual Review of Statistics and Its Application ( IF 7.4 ) Pub Date : 2022-03-07 , DOI: 10.1146/annurev-statistics-040120-021023
Andrew C. Harvey ₁

Affiliation

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.

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