SCOREDRIVENMODELS.JL: A JULIA PACKAGE FOR GENERALIZED AUTOREGRESSIVE SCORE MODELS

Guilherme Bodin, Raphael Saavedra, C. Fernandes, A. Street
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引用次数: 2

Abstract

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.
SCOREDRIVENMODELS。一个用于广义自回归分数模型的Julia包
分数驱动模型,也称为广义自回归分数模型,代表了一类观测驱动的时间序列模型。它们具有强大的特性,例如能够对不同的条件分布进行建模,并在灵活的框架内考虑时变参数。在本文中,我们提出了ScoreDrivenModels。jl,一个开源的Julia包,用于使用分数驱动模型的框架来建模、预测和模拟时间序列。该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随时间变化或恒定。也可以考虑几种分布,包括Beta、指数、Gamma、对数正态、正态、泊松、Student’st和威布尔。所提供的接口是灵活的,允许感兴趣的用户实现任何期望的分布和参数化。
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