Generalized autoregressive score (GAS) models are a class of observation-driven time series models that employ the score to dynamically update time-varying parameters of the underlying probability distribution. GAS models have been extensively studied and numerous variants have been proposed in the literature to accommodate diverse data types and probability distributions. This paper introduces the gasmodel package, which has been designed to facilitate the estimation, forecasting, and simulation of a wide range of GAS models. The package provides a rich selection of distributions, offers flexible options for specifying dynamics, and allows to incorporate exogenous variables. Model estimation utilizes the maximum likelihood method.

翻译：广义自回归得分模型是一类观测驱动的时序模型，通过利用得分函数动态更新基础概率分布中的时变参数。该类模型已得到广泛研究，文献中针对不同数据类型与概率分布提出了多种变体。本文介绍gasmodel包，该包旨在促进各类广义自回归得分模型的估计、预测与模拟。该包提供丰富的分布选择、灵活的动态设定选项，并支持纳入外生变量。模型估计采用极大似然方法。