Nowcasting GDP with Machine Learning Models: Evidence from the US

Orientador(a): Marcelo Medeiros

This paper examines the use of Machine Learning (ML) models to compute estimates of current-quarter US Real GDP growth rate (nowcasts). The methods used can handle large data sets with unsynchronized release dates, and nowcasts are updated each time new data are released along the quarter. A pseudo-out-of-sample exercise is proposed to assess the forecasting performance and to analyze the variable selection pattern of these models. The ML method that deserves more attention is the Target Factor, which overcomes the usually adopted dynamic factor model for some predictions vintages in the quarter. We also analyze the variables selected, which are consistent between models and intuition