Forecasting Quarterly GDP Growth Rates Using Machine Learning Methods

Predicting current and near‐term macroeconomic developments using linear indicator models and factor models, together with Economic Tendency Survey data, is standard nowcasting practice. In the current article, it is investigated whether machine learning (ML) methods, when used together with a limited set of tendency survey confidence indicators, can improve the forecasts of Swedish quarterly GDP growth compared with linear indicator models and factor models. The results indicate that ML methods generally perform relatively well. In particular, gradient‐boosted regression trees, random forests, and multilayer perceptron models are identified as some of the best performing models. The results indicate that ML models can be fruitfully applied in macroeconomic forecasting without employing vast amounts of data. One factor contributing to this could be the ability of ML methods to capture nonlinearities. Results also indicate that, when implementing ML models, care should be taken in determining how often central model parameters should be tuned and estimated.