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.
Similar content being viewed by others
Code availability
Not applicable.
Notes
The data on the annual real GDP growth of Japan refer to those published by the World Bank and are obtained from https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG?locations=JP.
References
Biau, O., & D’Elia, A. (2010). Euro area GDP forecasting using large survey datasets: A random forest approach. Euroindicators working papers.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple classifier systems. MCS 2000. Lecture notes in computer science (Vol. 1857, pp. 1–15).
Emsia, E., & Coskuner, C. (2016). Economic growth prediction using optimized support vector machines. Computational Economics, 48(3), 453–462.
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. New York: Springer.
Inoue, A., & Kilian, L. (2008). How useful is bagging in forecasting economic time series? A case study of U.S. consumer price inflation. Journal of the American Statistical Association, 103(482), 511–522.
Jung, J.-K., Patnam, M., & Ter-Martirosyan, A. (2018). An algorithmic crystal ball: Forecasts-based on machine learning. IMF Working Papers. Washington, D.C.: International Monetary Fund.
Medeiros, M. C., Vasconcelos, G. F. R., Veiga, Á., & Zilberman, E. (2019). Forecasting inflation in a data-rich environment: The benefits of machine learning methods. Journal of Business & Economic Statistics,. https://doi.org/10.1080/07350015.2019.1637745.
Molinaro, A. M., Simon, R., & Pfeiffer, R. M. (2005). Prediction error estimation: A comparison of resampling methods. Bioinformatics, 21(15), 3301–3307.
Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87–106.
Plakandaras, V., Gupta, R., Gogas, P., & Papadimitriou, T. (2015). Forecasting the U.S. real house price index. Economic Modelling, 45, 259–267.
Probst, P., Wright, M. N., & Boulesteix, A. L. (2019). Hyperparameters and tuning strategies for random forest. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(3), e1301.
Richardson, A., Mulder, T., & Vehbi, T. (2018). Nowcasting New Zealand GDP using machine learning algorithms. CAMA Working Paper 47/2018. The Australian National University.
Sandri, M., & Zuccolotto, P. (2008). A bias correction algorithm for the Gini variable importance measure in classification trees. Journal of Computational and Graphical Statistics, 17(3), 611–628.
Tashman, L. (2000). Out-of-sample tests of forecasting accuracy: An analysis and review. International Journal of Forecasting, 16(4), 437–450.
Tiffin, A. (2016). Seeing in the dark: A machine-learning approach to nowcasting in Lebanon. IMF Working Papers. International Monetary Fund.
Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–28.
Acknowledgements
I would like to express my gratitude to Professor BAAK Saang Joon, Professor KONDO Yasushi, and Professor KONISHI Hideki of Waseda University for their invaluable comments and advice.
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
J.Y. is the sole author.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Availability of Data and Materials
Data sources are indicated in the manuscript.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yoon, J. Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach. Comput Econ 57, 247–265 (2021). https://doi.org/10.1007/s10614-020-10054-w
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10614-020-10054-w