  The nature of available economic data has changed fundamentally in the last
decade due to the economy's digitisation. With the prevalence of often black
box data-driven machine learning methods, there is a necessity to develop
interpretable machine learning methods that can conduct econometric inference,
helping policymakers leverage the new nature of economic data. We therefore
present a novel Variational Bayesian Inference approach to incorporate a
time-varying parameter auto-regressive model which is scalable for big data.
Our model is applied to a large blockchain dataset containing prices,
transactions of individual actors, analyzing transactional flows and price
movements on a very granular level. The model is extendable to any dataset
which can be modelled as a dynamical system. We further improve the simple
state-space modelling by introducing non-linearities in the forward model with
the help of machine learning architectures.
