Nowcasting Korean GDP growth using Machine Learning with Economic Policy Uncertainty feature = 경제정책 불확실성을 반영한 머신러닝 기반의 한국 경제성장률 당기예측 연구

다국어 초록 (Multilingual 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.

목차 (Table of Contents)

• 1. Introduction.......................................................................................................- 1 -
• 1.1. Background .............................................................................................- 1 -
• 1.2. Thesis Structure .......................................................................................- 2 -
• 2. Literature Review..............................................................................................- 3 -
• 2.1. Economic Policy Uncertainty..................................................................- 3 -
• 2.2. Traditional approach to nowcast GDP growth ........................................- 5 -
• 2.2.1. Estimating Dynamic Factor Model............................................. - 5 -
• 2.3. Machine Learning approach to nowcast GDP growth.............................- 7 -
• 3. Methodology ......................................................................................................- 8 -
• 3.1. Framework...............................................................................................- 8 -
• 3.2. Economic Features..................................................................................- 9 -
• 3.2.1. Expectation-maximization Algorithm ........................................ - 9 -
• 3.2.2. Applying Dynamic Factor Model ..............................................- 11 -
• 3.3. Forecasting Models ...............................................................................- 11 -
• 3.3.1. Linear Model .............................................................................- 11 -
• 3.3.2. Random Forest.......................................................................... - 12 -
• 3.3.3. Boosting models ....................................................................... - 13 -
• 3.3.4. Evaluation Method.................................................................... - 14 -
• 4. Empirical Results............................................................................................- 15 -
• 4.1. Data collection and preprocessing.........................................................- 15 -
• 4.1.1. Economic Dataset ..................................................................... - 16 -
• 4.1.2. EPU Dataset.............................................................................. - 16 -
• 4.2. Method Application...............................................................................- 18 -
• 4.3. Experimental Results.............................................................................- 19 -
• 5. Conclusion........................................................................................................- 22 -