Generalized Autoregressive Score Models in R: The GAS Package

David Ardia, Kris Boudt, Leopoldo Catania

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This paper presents the R package GAS for the analysis of time series under the generalized autoregressive score (GAS) framework of Creal, Koopman, and Lucas (2013) and Harvey (2013). The distinctive feature of the GAS approach is the use of the score function as the driver of time-variation in the parameters of non-linear models. The GAS package provides functions to simulate univariate and multivariate GAS processes, to estimate the GAS parameters and to make time series forecasts. We illustrate the use of the GAS package with a detailed case study on estimating the time-varying conditional densities of financial asset returns.

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