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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.

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File URL: http://mpra.ub.uni-muenchen.de/19895/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 19895.

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Date of creation: 05 Nov 2009
Date of revision: 10 Jan 2010
Handle: RePEc:pra:mprapa:19895

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Keywords: Poisson Regression; Search Engine; Google Insights; Aggregation; Normalization Effects; Scaling Effects;

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  1. Rossana, Robert J & Seater, John J, 1995. "Temporal Aggregation and Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(4), pages 441-51, October.
  2. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
  3. Rapach, David E, 2003. " International Evidence on the Long-Run Impact of Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 35(1), pages 23-48, February.
  4. Joseph E. Gagnon, 2008. "Inflation regimes and inflation expectations," Review, Federal Reserve Bank of St. Louis, issue May, pages 229-243.
  5. Cameron, A Colin & Windmeijer, Frank A G, 1996. "R-Squared Measures for Count Data Regression Models with Applications to Health-Care Utilization," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 209-20, April.
  6. Michener, Ron & Tighe, Carla, 1992. "A Poisson Regression Model of Highway Fatalities," American Economic Review, American Economic Association, vol. 82(2), pages 452-56, May.
  7. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
  8. Cameron, A Colin & Trivedi, Pravin K, 1986. "Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 1(1), pages 29-53, January.
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