论文标题
cocordrivenmodels.jl：用于广义自动回归分数模型的朱莉娅软件包
ScoreDrivenModels.jl: a Julia Package for Generalized Autoregressive Score Models
论文作者
论文摘要
得分驱动的模型，也称为广义自动回归分数模型，代表了一类观察驱动的时间序列模型。它们具有强大的属性，例如能够建模不同条件分布并考虑在灵活框架内的时变参数的能力。在本文中，我们介绍了使用得分驱动的模型的框架来建模，预测和模拟时间序列的开源朱莉娅朱莉娅软件包。该软件包在模型定义方面是灵活的，允许用户指定滞后结构以及哪些参数是时间变化或恒定的。还可以考虑几个分布，包括Beta，指数，伽玛，logNormal，正常，Poisson，Student's T和Weibull。提供的接口是灵活的，使感兴趣的用户可以实现任何所需的分布和参数化。
Score-driven models, also known as generalized autoregressive score models, represent a class of observation-driven time series models. They possess powerful properties, such as the ability to model different conditional distributions and to consider time-varying parameters within a flexible framework. In this paper, we present ScoreDrivenModels.jl, an open-source Julia package for modeling, forecasting, and simulating time series using the framework of score-driven models. The package is flexible with respect to model definition, allowing the user to specify the lag structure and which parameters are time-varying or constant. It is also possible to consider several distributions, including Beta, Exponential, Gamma, Lognormal, Normal, Poisson, Student's t, and Weibull. The provided interface is flexible, allowing interested users to implement any desired distribution and parametrization.
