A Scalable Inference Method For Large Dynamic Economic Systems

• URL: http://arxiv.org/abs/2110.14346v1
• Date: Wed, 27 Oct 2021 10:52:17 GMT
• Title: A Scalable Inference Method For Large Dynamic Economic Systems
• Authors: Pratha Khandelwal, Philip Nadler, Rossella Arcucci, William Knottenbelt, Yi-Ke Guo
• Abstract summary: We present a novel Variational Bayesian Inference approach to incorporate a time-varying parameter auto-regressive model. Our model is applied to a large blockchain dataset containing prices, transactions of individual actors, analyzing transactional flows and price movements. We further improve the simple state-space modelling by introducing non-linearities in the forward model with the help of machine learning architectures.
• Score: 19.757929782329892
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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