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The Effects of Temporal Aggregation on Search Engine Data

Author

Listed:
  • Tierney, Heather L.R.
  • Kim, Jiyoon (June)
  • Nazarov, Zafar

Abstract

Using structured machine learning, this paper examines the effect that temporal aggregation has on big data from Google Analytics and Google Trends. Specifically, daily and weekly data from the Charleston Area Convention and Visitors Bureau (CACVB) website from January 2008 to March 2009 via Google Analytics and weekly, monthly, and quarterly data from Google Trends for seven economic variables from 2004 to 2011 are examined. Taking into account the different levels of aggregation, the CDFs and the estimated regression results are examined. 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 variable. Through data mining, this paper also finds that aggregation has the potential of affecting the level of integration and the regression results for both the CACVB data and the seven economic variables.

Suggested Citation

  • Tierney, Heather L.R. & Kim, Jiyoon (June) & Nazarov, Zafar, 2018. "The Effects of Temporal Aggregation on Search Engine Data," MPRA Paper 84474, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:84474
    as

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    File URL: https://mpra.ub.uni-muenchen.de/84474/1/MPRA_paper_84474.pdf
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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.
    2. Granger, C. W. J. & Siklos, Pierre L., 1995. "Systematic sampling, temporal aggregation, seasonal adjustment, and cointegration theory and evidence," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 357-369.
    3. Jaroslav Pavlicek & Ladislav Kristoufek, 2015. "Nowcasting Unemployment Rates with Google Searches: Evidence from the Visegrad Group Countries," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-11, May.
    4. Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-136, January.
    Full references (including those not matched with items on IDEAS)

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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:

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

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