Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach

Jaehyun Yoon*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)


This paper presents a method for creating machine learning models, specifically a gradient boosting model and a random forest model, to forecast real GDP growth. This study focuses on the real GDP growth of Japan and produces forecasts for the years from 2001 to 2018. The forecasts by the International Monetary Fund and Bank of Japan are used as benchmarks. To improve out-of-sample prediction, the cross-validation process, which is designed to choose the optimal hyperparameters, is used. The accuracy of the forecast is measured by mean absolute percentage error and root squared mean error. The results of this paper show that for the 2001–2018 period, the forecasts by the gradient boosting model and random forest model are more accurate than the benchmark forecasts. Between the gradient boosting and random forest models, the gradient boosting model turns out to be more accurate. This study encourages increasing the use of machine learning models in macroeconomic forecasting.

Original languageEnglish
Pages (from-to)247-265
Number of pages19
JournalComputational Economics
Issue number1
Publication statusPublished - 2021 Jan


  • Gradient boosting
  • Machine learning
  • Macroeconomic forecast
  • Random forest
  • Real GDP growth

ASJC Scopus subject areas

  • Economics, Econometrics and Finance (miscellaneous)
  • Computer Science Applications


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