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Generalized Autoregressive Score (GAS) models, also known as Dynamic Conditional Score (DCS) models, provide a general framework for modeling time variation in parametric models. The key features are:

- easy estimation and inference: the likelihood is available in closed form;
- generality: you are in business whenever you can compute the score of your parametric conditional observation density with respect to the time varying parameter.

On this site you find:

- some
**background**information about GAS models; - the
**papers**about GAS and score driven models that we are aware of; **computer code**for GAS models to help you get started;

- Oct 7, 2017: added 15 new entry and updates to the
**papers**section after some googling over 2016 and 2017. - Sep 6, 2017: added three new entry in the
**papers**section: modeling VaR and ES by scores (Patton et al., 2017), portfolio selection based on scores (Bernardi and Catania, 2016), and extreme values modeling using scores (Chong et al., 2017). - Sep 4, 2017: added a new entry in the
**papers**section on modeling time varying tail shapes: Massacci (in press). - Sep 4, 2017: added a new entry in the
**papers**section on modeling crypto-currencies with GAS models: Catania and Grassi (2017). - Sep 4, 2017: updated the GAS HMM package of Marco Bazzi in the
**code**section. - Jul 6, 2017: added two new entries in the
**papers**section on accellerated GAS and on GAS for football matches and GAS for option pricing: Koopman and Lit (2017) and Blasques, Gorgi, Koopman (2017) and Lin and Yu (2017). - Jul 4, 2017: David Kranenburg and Rutger Lit improved the example matlab code for the GAS volatility model (considerably faster without globals).
- May 31, 2017: added three new entries in the
**papers**section by Blazsek et al. 2016, 2017): time varying volatility, regime switching, and time varying degrees of freedom. - May 16, 2017: updated and added entries in the
**papers**section, including Neves et al. on GAS for actuarial models. - Feb 16, 2017: new entries in the
**papers**section: added the Oh and Patton paper forthcoming in JBES. - Feb 9, 2017: new entries in the
**papers**section: working paper of Buccheri et al. (2017) on GAS for high-frequency and synchronicity. - Feb 6, 2017: two new entries in the
**papers**section: Billé and Catania forthcoming in*Journal of Applied Econometrics*and working paper by Catania and Nonejad. - Dec 12, 2016: a new entry in the
**papers**section: Delle Monache and Petrella forthcoming in*International Journal of Forecasting*and Van Wijnbergen and Zhao in*Quantitative Finance*. - Nov 25, 2016: a new entry in the
**papers**section: Boswijk and Liu (2016): on score driven factor volatility models. - Nov 25, 2016: a new entry in the
**papers**section: Ardia, Boudt, and Catania (2016a,b): GAS applications in R. - Nov 14, 2016: the main GAS paper in Journal of Applied Econometrics has been made open access, see
**here**. - Nov 8, 2016: a new entry has appeared in the
**papers**section: Delle Monache, Petrella, Venditti (2016) on GAS for state space models. - Nov 8, 2016: a new entry in the
**papers**section: Lucas, Opschoor, Schaumburg (2016) now accepted in*Economics Letters* - Sep 19, 2016: new code for simulation and estimation of HMM models by Marco Bazzi in
**code**section. - Sep 14, 2016: updated
**paper**: Opschoor, Janus, Lucas, van Dijk now accepted for*Journal of Business and Economic Statistics*; joint multivariate models for returns and realized covariance matrices. - Sep 8, 2016: a new entry has appeared in the
**papers**section: Gorgi (2016) on integer valued gas models. - Sep 7, 2016: a new entry has appeared in the
**papers**section: Lucas, Opschoor (2016) on multivariate fractional integration. - Sep 2, 2016: a new entry has appeared in the
**papers**section: Creal, Drew, Siem Jan Koopman, Andre Lucas, and Marcin Zamojski (2015). - Sep 2, 2016: a new entry has appeared in the
**papers**section: Blasques, Francisco, Siem Jan Koopman, Andre Lucas and Julia Schaumburg (2016).