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

Tierney, Heather L. R. and Pan, Bing (2009): A Poisson Regression Examination of the Relationship between Website Traffic and Search Engine Queries.

Abstract

A new area of research involves the use of Google data, which has been normalized and scaled to predict economic activity. 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. Daily and weekly data are used to discuss the effects that normalization, scaling, and aggregation have on the empirical findings, which are frequency-dependent.

• Item Type: MPRA Paper
• Language: English
• Keywords: Poisson Regression, Search Engine, Google Insights, Aggregation
• 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 ; Unawareness; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C25 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities

References

Askitas, N. and Zimmerman, K.F. (2009), “Google Econometrics and Unemployment Forecasting,” Applied Economics Quarterly, 55:2, 107-120.

Azar, J. (2009), “Oil Prices and Electric Cars”, Princeton University Working Paper.

Barbaro, M. and Zeller, T. (2006) “A Face Is Exposed for AOL Searcher No. 4417749”, New York Times, August 9, accessed online at http://www.nytimes.com.

Bian, L. (1997), “Multiscale Nature of Spatial Data in Scaling Up Environmental Models,” Scale in Remote Sensing and GIS, D.A. Quattrochi and M.F. Goodchild, eds., London: CRC Press.

Cameron, A.C. and Trivedi, P.K. (1998), Regression Analysis of Count Data, Cambridge: Cambridge University Press.

Choi, H. and Varian, H. (2009a), “Predicting the Present with Google Trends,” Google Technical Report.

Choi, H. and Varian, H. (2009b), “Predicting Initial Claims for Unemployment Benefits,” Google Technical Report.

Engle, R. F. and Granger, C. W. J. (1987), “Co-Integration and Error Correction: Representation, Estimation, and Testing,” Econometrica, 55:2, 251-276.

Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., Brilliant, L. (2009), “Detecting Influenza Epidemics using Search Engine Query Data,” Nature, 457, 1012 –1014.

Google (2009), “About Google Trends,” http://www.google.com/intl/en/trends/about.html (accessed August 30, 2009).

Google Insights (2009a), “How is the data normalized?” http://www.google.com/support/insights/bin/bin/bin/answer.py?answer=87284&topic=13975 (accessed August 30, 2009).

Google Insights (2009b), “How is the data scaled?” http://www.google.com/support/insights/bin/bin/answer.py?answer=87282 (accessed August 30, 2009).

Pyle, D. (1999), “Data Preparation for Data Mining (The Morgan Kaufmann Series in Data Management Systems),” San Francisco: Morgan Kaufmann.

Rossana, R.J. and Seater, J.J. (1995) “Temporal Aggregation and Economic Time Series,” American Statistical Association, 13:4, 441-451. Michener, R. and Tighe, C. (1992), “A Poisson Regression Model of Highway Fatalities,” The American Economic Review, 82:2, 452-456

Wooldridge, J.M. (1999), “Quasi-Likelihood Methods for Count Data,” Handbook of Applied Econometrics, Volume II: Microeconomics, M.H. Pesaran and P. Schmidt, eds., Chichester: Blackwell.

Wooldridge, J.M. (2002), “Econometric Analysis of Cross Section and Panel Data,” Cambridge: MIT Press.