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 paper addresses the issue of estimating monthly gross domestic product 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. Our base model handles mixed frequency data and ragged-edge data structure with any pattern of missingness.

We estimate monthly GDP according to both the direct and the indirect approach. In the latter case we estimate sixteen GDP components, by output and expenditure type, by pooling the monthly estimates arising from all possible bivariate models and we aggregate them using a methodology, the annual overlap method, that is used in the production of the national accounts, and that ensures the consistency in cross-sectional aggregation with the published total GDP at market prices. Our methodology allows to assess the contribution of the monthly indicators to the estimation of monthly GDP, thereby providing essential information on their relevance. This evaluation has led to several interesting discoveries. Furthermore, the indirect approach enables us to present a growth accounting framework.