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Forecasting Chinese GDP Using Online Data

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  • Taoxiong Liu
  • Xiaofei Xu
  • Fangda Fan

Abstract

Because big data are widely used today, whether and how to use big data in macroeconomic forecasting has become a new field of economic research. In macroeconomic analyses, two types of data can be applied, namely, structured data and unstructured information. Statistical government data are well-structured, whereas Internet search behavior information, which is representative of online data, is unstructured. This article explores whether Internet search behavior information can facilitate the forecasting of macroeconomic aggregates and components and analyzes the use of feasible methods of structured data and unstructured information. This study is based on the macroeconomic forecasting model and verifies the effect of the two-step method. We find that Internet search behavior information can help forecast the macro economy, and we determine that the best method for variable selection using structured and unstructured data is the two-step method. First, only statistical government data are used, and temporary optimal models are selected. Second, Internet search behavior information are added to these models, and the optimal model is then determined.

Suggested Citation

  • Taoxiong Liu & Xiaofei Xu & Fangda Fan, 2018. "Forecasting Chinese GDP Using Online Data," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(4), pages 733-746, March.
    • Handle: RePEc:mes:emfitr:v:54:y:2018:i:4:p:733-746
    DOI: 10.1080/1540496X.2016.1216841
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    Cited by:

    1. K. M. Salah Uddin & Nishat Tanzim, 2023. "Forecasting GDP of Bangladesh Using ARIMA Model," International Journal of Business and Management, Canadian Center of Science and Education, vol. 16(6), pages 1-56, February.
    2. Benedikt Maas, 2020. "Short‐term forecasting of the US unemployment rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 394-411, April.

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