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AR Model or Machine Learning for Forecasting GDP and Consumer Price for G7 Countries

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  • Yutaka Kurihara
  • Akio Fukushima

Abstract

This paper examines the validity of forecasting economic variables by using machine learning. AI (artificial intelligence) has been improved and entering our society rapidly, and the economic forecast is no exception. In the real business world, AI has been used for economic forecasts, but not so many studies focus on machine learning. Machine learning is focused in this paper and a traditional statistical model, the autoregressive (AR) model is also used for comparison. A comparison of using an AR model and machine learning (LSTM) to forecast GDP and consumer price is conducted using recent cases from G7 countries. The empirical results show that the traditional forecasting AR model is a little more appropriate than the machine learning model, however, there is little difference to forecast consumer price between them.

Suggested Citation

  • Yutaka Kurihara & Akio Fukushima, 2019. "AR Model or Machine Learning for Forecasting GDP and Consumer Price for G7 Countries," Applied Economics and Finance, Redfame publishing, vol. 6(3), pages 1-6, May.
    • Handle: RePEc:rfa:aefjnl:v:6:y:2019:i:3:p:1-6
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    References listed on IDEAS

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    Cited by:

    1. Daniel Hopp, 2021. "Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)," Papers 2106.08901, arXiv.org.

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    More about this item

    Keywords

    AI; AR; consumer price; GDP; machine learning;
    All these keywords.

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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