Big data for policymaking in economics and finance: the potential and challenges

Aurel Schubert

INTRODUCTION

The state of the economy and its likely development in the near future have always been central for both economic policymaking and central banking. What has changed, however, in recent years (and in particular since the global financial crisis (GFC) of 2007–9) is the speed and frequency with which policy decisions have to be made. the complexity and heterogeneity of the economy and the ways of measuring consistently and reliably have risen dramatically in parallel. The reliance on a multitude of very diverse data sources (mainly surveys) with their respective time frames and the obligation to limit the reporting burden obstructs publication of results due to rather long production processes. This creates a conflict between the need for fast (and reliable) information and the need for a thorough production process. In addition, the GFC clearly highlighted the informational limits of aggregated data. Crucial tail risks, the “black swans” (Taleb 2007), were hidden inside rather “normal looking” aggregates.11 “Black swans” are a metaphor for events that are very rare, come as a surprise and have a major effect. Data of the necessary granularity was often not available

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