Which one predicts better? Comparing different GDP Nowcasting methods using Brazillian Data
Dynamic Factor models, LASSO, RIDGE, RNN, MIDAS, time series, macroeconometrics, nowcasting
This work has the primary objective of raising quantitative tools for assembling a scalable real-time GDP tracking for the Southern Cone countries, Nowcasting. In this work, we survey the literature since the first work on estimating business cycles and document the evolution of this literature until the insertion of machine learning methods used contemporaneously. Additionally, we perform exercises with an updated Brazilian database, estimate several candidate models for GDP nowcasting, implementing the division of classical models and machine learning models. Finally, we use the Diebold Mariano test to evaluate the forecasts of all models against a naive model and demonstrate that a combination of machine learning models based on the distance of forecasts to the average FOCUS expectations defeats the fully informed market expectations of the FOCUS survey, while the same is not possible for classical nowcasting models.