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Using Baidu Index To Nowcast Mobile Phone Sales In China

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  • JIANCHUN FANG

    (School of Economics, Zhejiang University of Technology, Hangzhou, Zhejiang Province 310023, P. R. China†Institute of World Economics and Politics, Chinese Academy of Social Sciences, Beijing 100732, P. R. China‡School of Economics, Zhejiang University, Hangzhou, Zhejiang Province 310027, P. R. China)

  • WANSHAN WU

    (School of Economics, Zhejiang University of Technology, Hangzhou, Zhejiang Province 310023, P. R. China‡School of Economics, Zhejiang University, Hangzhou, Zhejiang Province 310027, P. R. China)

  • ZHOU LU

    (#xA7;School of Economics, Tianjin University of Commerce, Tianjin City 300134, P. R. China)

  • EUNHO CHO

    (#xB6;School of Accounting and Finance, Kean University, Zhejiang Province 325060, P. R. China)

Abstract

Most of the official data are released with a lag period, which increases the difficulties for decision-makers assessing the situation. To solve the problem of data lag, we used real-time Baidu Index to nowcast the Chinese consumer behavior of buying the best-selling smartphone, Huawei Mate 7. We introduced keywords like “Mate 7” and “Huawei” in Baidu Index search queries to examine whether the introduction of real-time data can improve the efficiency of benchmark model. Overall, our finding is that the introduction of Baidu Index, both in-sample and out-of-sample, can improve the prediction accuracy of the model significantly. The extended model provided a 55.2% outperformance relative to benchmark one. This can not only make up for official data release lag, but also help firms gain near-real-time insight into the consumer demand trends and reduce inventory costs. The findings suggest that firms can improve marketing performance by use of search engine promotion campaign.

Suggested Citation

  • Jianchun Fang & Wanshan Wu & Zhou Lu & Eunho Cho, 2019. "Using Baidu Index To Nowcast Mobile Phone Sales In China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(01), pages 83-96, March.
    • Handle: RePEc:wsi:serxxx:v:64:y:2019:i:01:n:s021759081743007x
    DOI: 10.1142/S021759081743007X
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    Cited by:

    1. Zhongchen Song & Tom Coupé, 2023. "Predicting Chinese consumption series with Baidu," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 21(3), pages 429-463, July.
    2. Chuan Zhang & Yu-Xin Tian & Ling-Wei Fan, 2020. "Improving the Bass model’s predictive power through online reviews, search traffic and macroeconomic data," Annals of Operations Research, Springer, vol. 295(2), pages 881-922, December.
    3. Muhammad Tanveer & Harsandaldeep Kaur & George Thomas & Haider Mahmood & Mandakini Paruthi & Zhang Yu, 2021. "Mobile Phone Buying Decisions among Young Adults: An Empirical Study of Influencing Factors," Sustainability, MDPI, vol. 13(19), pages 1-18, September.

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    Keywords

    Search query data; Huawei; MIDAS;
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