Title: Nowcasting monthly GDP with big data: A model averaging approach
Authors: Alessandro Giovannelli - University of Rome Tor Vergata (Italy) [presenting]
Tommaso Proietti - University of Roma Tor Vergata (Italy)
Abstract: Gross domestic product (GDP) is the most comprehensive and authoritative measure of economic activity. The macroeconomic literature has focused on nowcasting and forecasting this measure at the monthly frequency, using related high frequency indicators. The issue of estimating monthly gross domestic product is addressed by using a large dimensional set of monthly indicators, by pooling the disaggregate estimates arising from simple and feasible bivariate models that consider one indicator at a time, in conjunction to GDP or a component of GDP. The weights used for the combination reflect the ability to nowcast the original quarterly GDP component. The base model handles mixed frequency data and ragged-edge data structure with any pattern of missingness. The methodology allows us to assess the contribution of the monthly indicators to the estimation of monthly GDP, thereby providing essential information on their relevance. This evaluation leads to several interesting discoveries.