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

Kakuho Furukawa, Ryohei Hisano, Yukio Minoura and Tomoyuki Yagi
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Kakuho Furukawa: Bank of Japan
Ryohei Hisano: The University of Tokyo
Yukio Minoura: Bank of Japan
Tomoyuki Yagi: Bank of Japan

No 22-E-16, Bank of Japan Working Paper Series from Bank of Japan

Abstract: 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.

Keywords: Industrial production; Mobility data; Electricity data; Nowcasting; Machine learning; COVID-19 (search for similar items in EconPapers)
JEL-codes: C49 C55 E23 E27 (search for similar items in EconPapers)
Date: 2022-11-25
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ene
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Citations: View citations in EconPapers (2)

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