Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth

We run a 'horse race' among popular forecasting methods, including machine learning (ML) and deep learning (DL) methods, employed to forecast U.S. GDP growth. Given the unstable nature of GDP growth data, we implement a recursive forecasting strategy to calculate the out-of-sample performance metrics of forecasts for multiple subperiods. We use three sets of predictors: a large set of 224 predictors [of U.S. GDP growth] taken from a large quarterly macroeconomic database (namely, FRED-QD), a small set of nine strong predictors selected from the large set, and another small set including these nine strong predictors together with a high-frequency business condition index. We then obtain the following three main findings: (1) when forecasting with a large number of predictors with mixed predictive power, density-based ML methods (such as bagging or boosting) can outperform sparsity-based methods (such as Lasso) for long-horizon forecast, but this is not necessarily the case for short-horizon forecast; (2) density-based ML methods tend to perform better with a large set of predictors than with a small subset of strong predictors; and (3) parsimonious models using a strong high-frequency predictor can outperform sophisticated ML and DL models using a large number of low-frequency predictors, highlighting the important role of predictors in economic forecasting. We also find that ensemble ML methods (which are the special cases of density-based ML methods) can outperform popular DL methods.