Constructing GDP Nowcasting Models Using Alternative Data

With coronavirus (COVID-19) having a significant impact on economic activity, the existing GDP nowcasting model, using only monthly and quarterly economic data, has become difficult to forecast with high accuracy. In this paper, we attempt to improve the accuracy of GDP nowcasting models by using alternative data that are available more promptly. Specifically, we construct nowcasting models that incorporate sparse estimation by Elastic Net using weekly retail sales data and hundreds of daily Internet search volume data, in addition to conventional monthly economic data. For the model formulation and data selection, we prepare a large number of candidate models using the method of forecast combination, which combines multiple forecasting models, and select "Best models" which minimize the forecast error, including data after the spread of COVID-19. The analysis shows that the use of alternative data significantly improves the forecasting accuracy of the model, especially at the 2-month prior to release of GDP, when the availability of monthly and quarterly economic data are limited.