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A Nowcasting Model of Industrial Production using Alternative Data and Machine Learning Approaches

November 25, 2022
Kakuho Furukawa*1
Ryohei Hisano*2
Yukio Minoura*3
Tomoyuki Yagi*4


Recent years have seen a growing trend to utilize "alternative data" in addition to traditional statistical data in order to understand and assess economic conditions in real time. In this paper, we construct a nowcasting model for the Indices of Industrial Production (IIP), which measure production activity in the manufacturing sector in Japan. The model has the following characteristics: First, it uses alternative data (mobility data and electricity demand data) that is available in real-time and can nowcast the IIP one to two months before their official release. Second, the model employs machine learning techniques to improve the nowcasting accuracy by endogenously changing the mixing ratio of nowcast values based on traditional economic statistics (the Indices of Industrial Production Forecast) and nowcast values based on alternative data, depending on the economic situation. The estimation results show that by applying machine learning techniques to alternative data, production activity can be nowcasted with high accuracy, including when it went through large fluctuations during the spread of the COVID-19 pandemic.

JEL Classification
C49, C55, E23, E27

Industrial production, Mobility data, Electricity data, Nowcasting, Machine learning, COVID-19.

The authors thank Kosuke Aoki, Ryo Jinnai, Seisaku Kameda, Jouchi Nakajima, and staff members of the Bank of Japan for their valuable comments. All remaining errors are our own. The views expressed in this paper are those of the authors and do not necessarily reflect the official views of the Bank of Japan.

  1. *1Research and Statistics Department, Bank of Japan
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  2. *2The University of Tokyo
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  3. *3Research and Statistics Department (currently Financial System and Bank Examination Department), Bank of Japan
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  4. *4Research and Statistics Department, Bank of Japan
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