2021/10/27 by Pratha Khandelwal, Khandelwal, Pratha, Philip Nadler +9
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Artificial Intelligence (cs.AI) #Blockchain Technology Applications and Security #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Market Dynamics and Volatility #Stock Market Forecasting Methods #cs.AI #cs.LG #econ.EM #stat.ML
paper · pdf · doi:10.48550/arxiv.2110.14346
arxiv created 2021/10/27 · openalex publication_date 2021/10/27 · arxiv updated 2021/10/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.