Nowcasting Korean GDP growth using Machine Learning with Economic Policy Uncertainty feature

Abstract

GDP growth is an indicator of a country's economic situation and is a crucial factor in financial decisions. Nevertheless, since it has a problem of being announced lately, 'Nowcasting', the prediction of GDP growth at present, is being treated as an essential issue. Due to the recent increase in uncertainty, studies to increase the accuracy of Nowcasting are primarily divided into two directions. One is to reflect uncertainty as a variable, and the other direction is to use ML models as predictive models. However, there has yet to be an attempt to incorporate both approaches. Therefore, this study aims to integrate both approaches to generate a prediction model for the GDP of Korea. The proposed method first extracts common factors through the Dynamic Factor Model to reduce the dimensions of 83 economic indicators affecting GDP growth. Then, the Economic Policy Uncertainty value, an indicator of uncertainty, is combined with the reduced factors, and they are used as input features of prediction models. Finally, several machine learning models are used to predict GDPs. To validate the proposed approach, we conduct experiments with Korean GDP-related data. In the experiment, we construct two data sets with and without the Economic Policy Uncertainty value to explore the impact of the uncertainty. Random Forest, Gradient Boost, and XGBoost are used as ML-based prediction models, while OLS regression is used as a conventional prediction model. The experimental result shows that including the EPU feature provides higher prediction accuracies for all four models. In addition, the performances of the ML models are more elevated than that of OSL regression.