ScoreDrivenModels.jl: a Julia Package for Generalized Autoregressive Score Models

TL;DR

ScoreDrivenModels.jl is an open-source Julia package that enables flexible modeling, forecasting, and simulation of time series using score-driven models with various distributions and customizable parameters.

Contribution

The paper introduces ScoreDrivenModels.jl, a versatile Julia package that simplifies implementation of generalized autoregressive score models with customizable distributions and model structures.

Findings

Supports multiple distributions including Beta, Exponential, Gamma, and more.

Allows flexible model specification with customizable lag structure and parameters.

Facilitates easy implementation and extension for different score-driven modeling needs.

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.