A Scalable Inference Method For Large Dynamic Economic Systems
This work addresses the need for interpretable machine learning methods to help policymakers leverage digitized economic data, though it appears incremental by extending state-space modeling with non-linearities.
The authors tackled the challenge of conducting interpretable econometric inference on large-scale dynamic economic data by developing a scalable Variational Bayesian Inference method for time-varying parameter auto-regressive models, applied to a blockchain dataset to analyze transactional flows and price movements at a granular level.
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.