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A Poisson Regression Examination of the Relationship between Website Traffic and Search Engine Queries

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  • Tierney, Heather L.R.
  • Pan, Bing

Abstract

A new area of research involves the use of Google data, which has been normalized and scaled to predict economic activity. This new source of data holds both many advantages as well as disadvantages, which are discussed through the use of daily and weekly data. Daily and weekly data are employed to show the effect of aggregation as it pertains to Google data, which can lead to contradictory findings. In this paper, Poisson regressions are used to explore the relationship between the online traffic to a specific website and the search volumes for certain keyword search queries, along with the rankings of that specific website for those queries. The purpose of this paper is to point out the benefits and the pitfalls of a potential new source of data that lacks transparency in regards to the original level data, which is due to the normalization and scaling procedures utilized by Google.

Suggested Citation

  • Tierney, Heather L.R. & Pan, Bing, 2009. "A Poisson Regression Examination of the Relationship between Website Traffic and Search Engine Queries," MPRA Paper 19895, University Library of Munich, Germany, revised 10 Jan 2010.
    • Handle: RePEc:pra:mprapa:19895
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    References listed on IDEAS

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    Cited by:

    1. Evangelos Mourelatos & Manolis Tzagarakis, 2018. "An investigation of factors affecting the visits of online crowdsourcing and labor platforms," Netnomics, Springer, vol. 19(3), pages 95-130, December.
    2. Ying Liu & Yibing Chen & Sheng Wu & Geng Peng & Benfu Lv, 2015. "Composite leading search index: a preprocessing method of internet search data for stock trends prediction," Annals of Operations Research, Springer, vol. 234(1), pages 77-94, November.

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    More about this item

    Keywords

    Poisson Regression; Search Engine; Google Insights; Aggregation; Normalization Effects; Scaling Effects;
    All these keywords.

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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