[Vol.28 No.2] Dynamic Factor Model and Deep Learning Algorithm for GDP Nowcasting

GDP nowcasting

Author: Hyun Chang Yi(Bank of Korea), Dongkyu Choi(Bank of Korea), Yonggun Kim(Bank of Korea)

 As economic uncertainty increases due to the COVID-19 pandemic and climate change, indicators that can judge the economic situation in a timely manner are becoming more important. Although GDP growth is a representative indicator of the overall economic situation, there is a limit to quickly assessing the real-time economic situation due to publication lags and the low frequency nature. In this paper, we develop real- time GDP nowcasting system that updates the forecast every week. In this system, Long Short-Term Memory(LSTM), which is specialized in time series among deep learning algorithms, and Dynamic Factor Model(DFM) were complementarily used to enhance forecasting power.

 Both DFM and LSTM appropriately captured the actual economic situation. In particular, the rapid decline in the GDP growth rate in the 2020:Q1~Q2 due to the COVID-19 pandemic was detected early, and signs of a rebound in GDP growth in the 2020:Q3 was also quickly captured. In addition, the LSTM showed relatively high forecasting power in a situation where economic uncertainty has increased, such as the COVID-19 pandemic.

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