Presented by Ana Beatriz Galvão, University of Warwick
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 presents 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.
Presenter bio
Ana Beatriz Galvão is Professor of Economic Modelling and Forecasting at Warwick Business School, University of Warwick. Her research on empirical macroeconomics and forecasting has been widely published in leading academic journals such as Journal of Econometrics and the Journal of Business and Economics Statistics. Her recent research has focused on the measurement and communication of data uncertainty, asymmetries in the transmission of shocks to the macroeconomy, the impact of expert judgment in forecasting and applications of mixed frequency models. She is currently Associate Editor of the International Journal of Forecasting and the Journal of Applied Econometrics.
Discussant: Andrew Walton, UK Office for National Statistics
Chair: Stuart McIntyre, University of Strathclyde and ESCoE
