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Generalized Autoregressive Score Models in R: The GAS Package

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Abstract
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.
Keywords
FAT TAILS, DYNAMICS, GAS, time series models, score models, dynamic conditional score, R, software

Citation

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Chicago
Ardia, David, Kris Boudt, and Leopoldo Catania. 2019. “Generalized Autoregressive Score Models in R: The GAS Package.” Journal of Statistical Software 88 (6): 1–28.
APA
Ardia, D., Boudt, K., & Catania, L. (2019). Generalized Autoregressive Score Models in R: The GAS Package. JOURNAL OF STATISTICAL SOFTWARE, 88(6), 1–28.
Vancouver
1.
Ardia D, Boudt K, Catania L. Generalized Autoregressive Score Models in R: The GAS Package. JOURNAL OF STATISTICAL SOFTWARE. Los angeles: Journal Statistical Software; 2019;88(6):1–28.
MLA
Ardia, David, Kris Boudt, and Leopoldo Catania. “Generalized Autoregressive Score Models in R: The GAS Package.” JOURNAL OF STATISTICAL SOFTWARE 88.6 (2019): 1–28. Print.
@article{8600204,
  abstract     = {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.},
  author       = {Ardia, David and Boudt, Kris and Catania, Leopoldo},
  issn         = {1548-7660},
  journal      = {JOURNAL OF STATISTICAL SOFTWARE},
  language     = {eng},
  number       = {6},
  pages        = {1--28},
  publisher    = {Journal Statistical Software},
  title        = {Generalized Autoregressive Score Models in R: The GAS Package},
  url          = {http://dx.doi.org/10.18637/jss.v088.i06},
  volume       = {88},
  year         = {2019},
}

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