Modelling with Big Data and Machine Learning

A two-day workshop will be held to discuss recent scientific advances, as well as to connect policy makers and academics.

Date: 26 - 27 November 2018
Venue: Bank of England, London

The confluence of expanding access to data and the rapid advance of modelling techniques like those from machine learning promise 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 (FRB) held a two-day workshop to discuss recent advances in modelling the economy using big data and novel modelling approaches. 

The conference covered a range of topics including:

  • Machine Learning for prediction and understanding the economy
  • Methods (matching, filtering or cleaning techniques)
  • Theory (modelling, estimation with many covariates or strong non-linearities)
  • Large granular structured or unstructured data sources (administrative data, web data, from the “digital exhaust”, text data)
  • Big data topics covering businesses, households, finance, labour markets or government.

Keynote speakers and panellists included:

  • Domenico Giannone, Federal Reserve Bank of New York
  • Andrew Haldane, Chief Economist, BoE
  • Paul Ormerod, Volterra Partners
  • Rebecca Riley, Economics Statistics Centre of Excellence
  • George Kapetanios, King’s College London

The conference provided an opportunity to discuss recent scientific advances, as well as to connect with policy makers and academics.  

Event material is available to view here:

PDFProgramme

Other26 November presentations (57mb)

Other27 November presentations (16mb)

 

This page was last updated 07 December 2018
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