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Score-Driven Time Series Models
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DOI：10.1146/annurev-statistics-040120-021023.png)
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Abstract
En 中文
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 seriesmodeling, 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
Journal
IF:
8.7
Papers:
211
Citations:
2.4K