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Score-driven models, also known as generalized autoregressive score models,represent a class of observation-driven time series models. They possesspowerful properties, such as the ability to model different conditionaldistributions and to consider time-varying parameters within a flexibleframework. In this paper, we present ScoreDrivenModels.jl, an open-source Juliapackage for modeling, forecasting, and simulating time series using theframework of score-driven models. The package is flexible with respect to modeldefinition, allowing the user to specify the lag structure and which parametersare time-varying or constant. It is also possible to consider severaldistributions, including Beta, Exponential, Gamma, Lognormal, Normal, Poisson,Student's t, and Weibull. The provided interface is flexible, allowinginterested users to implement any desired distribution and parametrization.

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