Modelling with Big Data and Machine Learning: Interpretability and Model Uncertainty

The confluence of access to large granular data sources ('Big Data') and the rapid advance of modelling techniques like those from machine learning (ML) promises new insights into the economy and a larger information set for policymakers. The Bank of England (BoE) and the Data Analytics for Finance and Macro (DAFM) Research Centre at King's College London have recently initiated a series of annual scientific conferences to discuss these advances. Our events aim both to discuss recent developments and, crucially, focus on particular aspects of Big Data and ML approaches which are of increased interest to applied researchers. Two such aspects form the focus of this two-day conference. The first relates to a commonly cited weakness of ML methods when applied to economic problems and data, which is lack of interpretability of ML model outputs.