Tracking weekly activity using new data sources

Presented by Ana Beatriz Galvão, Bloomberg on 29 January 2026. Policymakers increasingly rely on real-time measures of economic activity to inform decisions, yet official statistics are typically available only with substantial delay. This webinar presented a methodology to extract information from high-frequency indicators in order to produce weekly estimates of official monthly statistics in real time. Building on real-time tracking and nowcasting models, the approach addresses two challenges inherent in alternative indicators: the absence of seasonal adjustment and the prevalence of outliers. Seasonal components are directly incorporated into the model, allowing the seasonal structure of low-frequency data to inform high frequency proxies, and employ fat-tailed distributions to mitigate the influence of large, infrequent shocks. Applying the methodology to UK data allows the tracking of retail sales, monthly GDP, and vacancies using proxies such as debit card spending (Revolut) and online job advertisements. Discussant: Andrew Walton, UK Office for National Statistics Chair: Stuart McIntyre, University of Strathclyde and ESCoE