Nowcasting Indonesia’s GDP Growth Using Machine Learning Algorithms


  • Nadya Dwi Muchisha Badan Pusat Statistik, Indonesia; Department of Statistics, IPB University, Indonesia
  • Novian Tamara Badan Pusat Statistik, Indonesia; Department of Statistics, IPB University, Indonesia
  • Andriansyah Fiscal Policy Agency, Ministry of Finance, Indonesia
  • Agus M Soleh Department of Statistics, IPB University, Indonesia



gdp growth, machine learning, nowcasting


GDP is very important to be monitored in real time because of its usefulness for policy making. We built and compared the ML models to forecast real-time Indonesia's GDP growth. We used 18 variables that consist a number of quarterly macroeconomic and financial market statistics. We have evaluated the performance of six popular ML algorithms, such as Random Forest, LASSO, Ridge, Elastic Net, Neural Networks, and Support Vector Machines, in doing real-time forecast on GDP growth from 2013:Q3 to 2019:Q4 period. We used the RMSE, MAD, and Pearson correlation coefficient as measurements of forecast accuracy. The results showed that the performance of all these models outperformed AR (1) benchmark. The individual model that showed the best performance is random forest. To gain more accurate forecast result, we run forecast combination using equal weighting and lasso regression. The best model was obtained from forecast combination using lasso regression with selected ML models, which are Random Forest, Ridge, Support Vector Machine, and Neural Network.


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A. Richardson, T. Mulder and T. Vehbi, "Nowcasting New Zealand GDP using Machine Learning Algorithms," in IFC - Bank Indonesia International Workshop and Seminar on , Bali, Indonesia, 2018.

N. Adriansson and I. Mattsson, Forecasting GDP Growth, or How Can Random Forests Improve Predictions in Economics? [Bachelor Thesis], Uppsala, Sweden: Uppsala University, 2015.

C. Chakraborty and A. Joseph, "Machine Learning at Central Banks," Bbank of England, London, 2017.

R. Tibshirani, "Regression Shrinkage and Selection via the Lasso," Journal of the Royal Statistical Society. Series B (Methodological), vol. 58, pp. 267-288, 1996.

H. Zou and T. Hastie, "Regularization and Variable Selection Via The Elastic Net," JR Statist Soc B, vol. 67, pp. 301-320, 2005.

L. Breiman, "Random Forests - Random Features," Barkeley, 1999.

T. Hastie, R. Tibshirani and J. Friendman, The Elements of Statistical Learning, Second Edition, Verlag: Springer, 2017.

K.-Y. Lee, K.-H. Kim, J.-J. Kang, S.-J. Choi, Y.-S. Im, L. Young-Dae and Y.-S. Lim, "Comparison and Analysis of Linear Regression & Artificial Neural Network," Research India Publications, p. 9821, 2017.

V. N. Vapnik, The Nature of Statistical Learning Theory, Berlin: Springer-Verlag, 1995.

R. Clemen, "Combining Forecasts: A Review and Annotated Bibliography," International Journal of Forecasting 5, pp. 559-583, 1989.




How to Cite

Muchisha, N. D. ., Tamara, N. ., Andriansyah, A., & Soleh, A. M. (2021). Nowcasting Indonesia’s GDP Growth Using Machine Learning Algorithms. Indonesian Journal of Statistics and Its Applications, 5(2), 355–368.




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