A Scalable Inference Method For Large Dynamic Economic Systems

TL;DR

This paper introduces a scalable Variational Bayesian Inference method for large dynamic economic systems, enabling interpretable analysis of big, complex, and time-varying data such as blockchain transactions.

Contribution

It develops a novel scalable inference approach for time-varying auto-regressive models, incorporating non-linearities with machine learning architectures for economic data analysis.

Findings

Successfully applied to large blockchain datasets

Enhanced model flexibility with non-linear extensions

Demonstrated scalability for big data applications

Abstract

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.