Score-Driven Time Series Models

The construction of score-driven filters for nonlinear time series models is described, and they are shown to apply over a wide range disciplines. Their theoretical practical advantages other methods highlighted. Topics covered include robust modeling, conditional heteroscedasticity, count data, dynamic correlation association, censoring, circular switching regimes.