Nowcasting Indonesia’s GDP Growth Using Machine Learning Algorithms
Nadya Dwi Muchisha,
Andriansyah Andriansyah () and
Agus M Soleh
MPRA Paper from University Library of Munich, Germany
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.
Keywords: Nowcasting; Indonesian GDP; Machine Learning (search for similar items in EconPapers)
JEL-codes: C55 E30 O40 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-mac and nep-sea
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