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

Author

Listed:
  • Kakuho Furukawa

    (Bank of Japan)

  • Ryohei Hisano

    (The University of Tokyo)

  • Yukio Minoura

    (Bank of Japan)

  • Tomoyuki Yagi

    (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.

Suggested Citation

  • Kakuho Furukawa & Ryohei Hisano & Yukio Minoura & Tomoyuki Yagi, 2022. "A Nowcasting Model of Industrial Production using Alternative Data and Machine Learning Approaches," Bank of Japan Working Paper Series 22-E-16, Bank of Japan.
    • Handle: RePEc:boj:bojwps:wp22e16
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    References listed on IDEAS

    as
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    3. Fornaro, Paolo, 2020. "Nowcasting Industrial Production Using Uncoventional Data Sources," ETLA Working Papers 80, The Research Institute of the Finnish Economy.
    4. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 4, pages 135-196, Elsevier.
    5. Takashi Nakazawa, 2022. "Constructing GDP Nowcasting Models Using Alternative Data," Bank of Japan Working Paper Series 22-E-9, Bank of Japan.
    6. Seisaku Kameda, 2022. "Use of Alternative Data in the Bank of Japan's Research Activities," Bank of Japan Review Series 22-E-1, Bank of Japan.
    7. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    8. James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Kakuho Furukawa & Ryohei Hisano, 2022. "A Nowcasting Model of Exports Using Maritime Big Data," Bank of Japan Working Paper Series 22-E-19, Bank of Japan.
    2. Marco Fruzzetti & Tiziano Ropele, 2024. "Nowcasting Italian industrial production: the predictive role of lubricant oils," Questioni di Economia e Finanza (Occasional Papers) 866, Bank of Italy, Economic Research and International Relations Area.

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    More about this item

    Keywords

    Industrial production; Mobility data; Electricity data; Nowcasting; Machine learning; COVID-19;
    All these keywords.

    JEL classification:

    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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