GAS
Generalized Autoregressive Score Models
Simulation, estimation and forecasting of univariate and
multivariate generalized autoregressive score models. Time-varying
distributional parameters are updated using the score of the conditional
distribution, with alternative score scaling specifications. Provides
maximum likelihood estimation, filtering, density forecasting and
rolling forecast evaluation for a range of conditional distributions.
Includes diagnostic tools and methods for evaluating Value-at-Risk and
Expected Shortfall forecasts. The models and their applications are
described in Ardia et al. (2019)
GAS
GAS implements the Generalized Autoregressive
Score (GAS) framework in R. The GAS package provides
functions to simulate univariate and multivariate GAS processes,
estimate the GAS parameters and to make time series forecasts. Full description of the algorithm and numerous applications are available in Ardia et al. (2018) and Ardia et al. (2019).
Please cite the package in publications!
By using GAS you agree to the following rules:
- You must cite Ardia et al. (2019) in working papers and published papers that use
GAS. - You must place the following URL in a footnote to help others find
GAS: https://CRAN.R-project.org/package=GAS. - You assume all risk for the use of
GAS.
Ardia, D., Boudt, K., Catania, L. (2018).
Downside Risk Evaluation with the R Package GAS.
R Journal, 10(2), 410-421.
https://doi.org/10.32614/RJ-2018-064
Ardia, D., Boudt, K., Catania, L. (2019).
Generalized autoregressive score models in R: The GAS package.
Journal of Statistical Software, 88(6), 1-28.
https://doi.org/10.18637/jss.v088.i06