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. Copyright Springer Science+Business Media New York 2012
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 13 (2012)
Issue (Month): 3 (October)
|Contact details of provider:|| Web page: http://www.springerlink.com/link.asp?id=102537|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- 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.
- Nikos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Discussion Papers of DIW Berlin 899, DIW Berlin, German Institute for Economic Research.
- Askitas, Nikos & Zimmermann, Klaus F., 2009. "Google Econometrics and Unemployment Forecasting," IZA Discussion Papers 4201, Institute for the Study of Labor (IZA).
- Nikos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Research Notes of the German Council for Social and Economic Data 41, German Council for Social and Economic Data (RatSWD).
- 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.
- Engle, Robert F & Granger, Clive W J, 1987.
"Co-integration and Error Correction: Representation, Estimation, and Testing,"
Econometric Society, vol. 55(2), pages 251-76, March.
- Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 39(3), pages 106-135.
- John J. Seater & Robert J. Rossana, .
"Temporal Aggregation and Economic Time Series,"
Working Paper Series
19, North Carolina State University, Department of Economics.
- Joseph E. Gagnon, 1997.
"Inflation Regimes and Inflation Expectations,"
RBA Research Discussion Papers
rdp9701, Reserve Bank of Australia.
- 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.
- Cameron, A.C. & Windmeijer, F.A.G., 1993. "R-Squared Measures for Count Data Regression Models with Applications to Health Care Utilization," Papers 93-24, California Davis - Institute of Governmental Affairs.
When requesting a correction, please mention this item's handle: RePEc:kap:netnom:v:13:y:2012:i:3:p:155-189. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla)or (Christopher F. Baum)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.