Score-driven time series models

Andrew Harvey

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

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 (search for similar items in EconPapers)
JEL-codes: C22 C32 (search for similar items in EconPapers)
Date: 2021-04-07
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
Note: ach34
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:2133

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