Nowcasting US GDP Using Tree-Based Ensemble Models and Dynamic Factors

In this study, we nowcast quarter-over-quarter US GDP growth rates between 2000Q2 and 2018Q4 using tree-based ensemble machine learning models, namely, bagged decision trees, random forests, and stochastic gradient tree boosting. To solve the ragged edge problem and reduce the dimension of the data set, we adopt a dynamic factor model. Dynamic factors extracted from 10 groups of financial and macroeconomic variables are fed to machine learning models for nowcasting US GDP. Our results show that tree-based ensemble models usually outperform linear dynamic factor models. Factors obtained from real variables appear to be more influential in machine learning models. The impact of factors derived from financial and price variables can only become important in predicting GDP after the great financial crisis of 2008-9, reflecting the effect extra loose monetary policies implemented in the period following the crisis.

Automated summary

The study shows that tree-based ensemble models generally outperform linear dynamic factor models, with factors derived from real variables having a stronger influence on machine learning models. Factors derived from financial and price variables only become important in predicting GDP after the 2008-9 global financial crisis, reflecting the impact of extra loose monetary policies implemented in the period following the crisis.