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Generalized Autoregressive Score Models
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In this article, a further development of Creal, Koopman, and Lucas (2012) which is based on the score function of the predictive model density at time t is discussed.Abstract:
To capture the dynamic behavior of univariate and multivariate time series processes, we can allow parameters to be time-varying by having them as functions of lagged dependent variables as well as exogenous variables. Although other approaches of introducing time dependence exists, the GAS models, Generalized Autoregressive Score, particular approach have become popular in applied statistics and econometrics. Here we discuss a further development of Creal, Koopman, and Lucas (2012) which is based on the score function of the predictive model density at time t.read more
Citations
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References
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Autoregressive conditional duration: a new model for irregularly spaced transaction data
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Modelling asymmetric exchange rate dependence
TL;DR: In this paper, the authors test for asymmetry in a model of the dependence between the Deutsche mark and the yen, in the sense that a different degree of correlation is exhibited during joint appreciations against the U.S. dollar versus during joint depreciations.
Journal ArticleDOI
Generalized autoregressive score models with applications
TL;DR: A unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models, referred to as Generalized Autoregressive Score (GAS) models, which encompasses other well-known models such as the generalized autoregressive conditional heteroskedasticity.