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

Heather Tierney and Bing Pan

MPRA Paper from University Library of Munich, Germany

Abstract: A new area of research involves the use of normalized and scaled Google search volume data to predict economic activity. This new source of data holds both many advantages as well as disadvantages. Daily and weekly data are employed to show the effect of aggregation in 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 search queries, along with the rankings of that 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 raw data, which is due to the normalization and scaling procedures utilized by Google.

Keywords: Poisson Regression; Search Engine; Google Insights; Aggregation; Normalization Effects; Scaling Effects (search for similar items in EconPapers)
JEL-codes: C25 C43 D83 (search for similar items in EconPapers)
Date: 2010-09-01, Revised 2011-07-08
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https://mpra.ub.uni-muenchen.de/32117/1/MPRA_paper_32117.pdf original version (application/pdf)

Related works:
Journal Article: A poisson regression examination of the relationship between website traffic and search engine queries (2012)
Working Paper: A Poisson Regression Examination of the Relationship between Website Traffic and Search Engine Queries (2010)
Working Paper: A Poisson Regression Examination of the Relationship between Website Traffic and Search Engine Queries (2009)
Working Paper: A Poisson Regression Examination of the Relationship between Website Traffic and Search Engine Queries (2009)
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