A Scalable Inference Method For Large Dynamic Economic Systems

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Tác giả: Rossella Arcucci, Yi-Ke Guo, Pratha Khandelwal, William Knottenbelt, Philip Nadler

Ngôn ngữ: eng

Ký hiệu phân loại: 001.434 Experimental method

Thông tin xuất bản: 2021

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Bộ sưu tập: Metadata

ID: 168089

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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