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

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  • Jaehyun Yoon

    (Waseda University)

Abstract

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.

Suggested Citation

  • Jaehyun Yoon, 2021. "Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 247-265, January.
    • Handle: RePEc:kap:compec:v:57:y:2021:i:1:d:10.1007_s10614-020-10054-w
    DOI: 10.1007/s10614-020-10054-w
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    15. Ba Chu & Shafiullah Qureshi, 2023. "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1567-1609, December.

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