A Poisson Regression Examination of the Relationship between Website Traffic and Search Engine Queries
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.
Year of publication: |
2010-09-01
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Authors: | Tierney, Heather L.R. ; Pan, Bing |
Saved in:
freely available
Type of publication: | Book / Working Paper |
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Language: | English |
Notes: | Tierney, Heather L.R. and Pan, Bing (2010): A Poisson Regression Examination of the Relationship between Website Traffic and Search Engine Queries. |
Classification: | C43 - Index Numbers and Aggregation ; D83 - Search, Learning, Information and Knowledge ; C25 - Discrete Regression and Qualitative Choice Models |
Source: | BASE |
Persistent link: https://www.econbiz.de/10015227707