Tierney, Heather L. R. and Pan, Bing (2009): A Poisson Regression Examination of the Relationship between Website Traffic and Search Engine Queries.
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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 procedure utilized by Google.
|Item Type:||MPRA Paper|
|Original Title:||A Poisson Regression Examination of the Relationship between Website Traffic and Search Engine Queries|
|Keywords:||Poisson Regression, Search Engine, Google Insights, Aggregation, Normalization Effects, Scaling Effects|
|Subjects:||C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C43 - Index Numbers and Aggregation
D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D83 - Search; Learning; Information and Knowledge; Communication; Belief
C - Mathematical and Quantitative Methods > C2 - Single Equation Models; Single Variables > C25 - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
|Depositing User:||Heather L.R. Tierney|
|Date Deposited:||28. Nov 2009 12:30|
|Last Modified:||11. Feb 2013 16:51|
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