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Score-Driven Time Series Models


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Working Paper

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Authors

Harvey, A. 

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.

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Keywords

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

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Publisher

Faculty of Economics, University of Cambridge

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