Samantha Robinson (University of Arkansas): Structural Functional Time Series Models

We propose a Bayesian model for functional time series that is based on an extension of the highly successful dynamic linear model to Banach state-valued data, discussing practical issues of discretization and simulation-based inference via MCMC. We discuss extensions of finite dimensional structural time series models to the functional setting, building a very convenient and flexible toolbox of standard functional dynamic linear models. The proposed models, together with the relative MCMC algorithms, are tested on two data sets: the first about electricity demand and the second about interest rates.

Joint work with Giovanni Petris, Samantha Robinson

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