Modelling with Big Data & Machine Learning: Measuring Economic Instability

The Bank of England, The Federal Reserve Board and King's College London present a joint virtual conference

About the event 

The Bank of England, The Federal Reserve Board and King’s College London held a joint virtual conference from the 4 – 6 November 2020.

The confluence of access to large granular data sources (‘Big Data’) and the rapid advance of modelling techniques like those from machine learning promises new insights into the economy and a larger information set for policymakers. The Bank of England (BoE), the Data Analytics for Finance and Macro (DAFM) Research Centre at King’s College London and the Federal Reserve Board have recently initiated a series of annual scientific conferences to discuss these advances and how they pertain to Measuring Economic Instability.

The Coronavirus pandemic and the widespread economic downturn in the wake of the resulting ‘lockdown’ in many countries have spurred an unprecedented output of research in multiple disciplines. This research is serving as a vital guide to policymakers in governments, central banks and international institutions around the globe as events unfold. Crucial roles in this information gathering and evaluation process are played by novel high-frequency and low-latency data sources, as well as non-conventional modelling techniques like machine learning, artificial intelligence, and interdisciplinary approaches such as those from epidemiology and economics. The conference aimed to provide an opportunity to discuss recent scientific advances, especially with a focus on aiming at quantifying potentially rapid economic fluctuations, and to connect policy makers and academia.

This page was last updated 11 January 2021

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