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The Effects of Temporal Aggregation on Search Engine Data

Author

Listed:
  • Heather L.R. Tierney

    (Economics Department, Doermer School of Business, Neff 340B Purdue University, Fort Wayne, 2101 East Coliseum Boulevard, Fort Wayne, IN, 46805, U.S.A.)

  • Jiyoon Kim

    (Economics Department, Doermer School of Business, Neff 340J Purdue University, Fort Wayne, 2101 East Coliseum Boulevard, Fort Wayne, IN, 46805, U.S.A.)

  • Zafar Nazarov

    (Economics Department, Doermer School of Business, Neff 340A Purdue University, Fort Wayne, 2101 East Coliseum Boulevard, Fort Wayne, IN, 46805, U.S.A.)

Abstract

Through the use of structured machine learning, this paper examines the effects of temporal aggregation on big data from Google Analytics and Google Trends. Google Analytics is used to obtain daily and weekly tourism data from the Charleston Area Convention and Visitors Bureau (CACVB) website, and Google Trends is used to obtain an index formed from big data of weekly, monthly, and quarterly data for seven economic variables. Taking into account the different levels of aggregation, the CDFs and the estimated structured machine learning output are used to study the effects of temporal aggregation. The Kolmogorov-Smirnov test rejects the null of equivalent data distributions in the vast majority of cases for the CACVB data, but this is not the case for the economic variables. Through data mining techniques, this paper also finds that the level of aggregation has the potential of affecting the level of integration and the estimated structured machine learning output for both the CACVB data and the seven economic variables.

Suggested Citation

  • Heather L.R. Tierney & Jiyoon Kim & Zafar Nazarov, 2018. "The Effects of Temporal Aggregation on Search Engine Data," Review of Economics & Finance, Better Advances Press, Canada, vol. 12, pages 57-71, May.
    • Handle: RePEc:bap:journl:180205
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    References listed on IDEAS

    as
    1. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
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    More about this item

    Keywords

    Big data; Machine learning; Data mining; Aggregation; Unit roots; Scaling effects; Normalization effects;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other

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