A population dependent diffusion model with a stochastic extension
AbstractDiffusion modeling is rather broad in nature, and is important in the areas of estimation and forecasting. Conventional models do not incorporate parameters that explicitly take into account the size of the population, or, equivalently, the size of the potential market. As a consequence, the models often fail to provide accurate forecasts, especially when the diffusion process is in the take-off stage. Furthermore, since diffusion is not a strictly deterministic process, forecasts should provide a measure of the underlying uncertainty of the process by incorporating a stochastic process into the formulation of the models.
Download InfoIf 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.
Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 28 (2012)
Issue (Month): 3 ()
Contact details of provider:
Web page: http://www.elsevier.com/locate/ijforecast
Innovation diffusion; High technology markets; Technology estimation and forecasting; ‘‘Population” diffusion model (PDM); Stochastic diffusion models (SDM);
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.:
- Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
- Albert C. Bemmaor & Janghyuk Lee, 2002. "The Impact of Heterogeneity and Ill-Conditioning on Diffusion Model Parameter Estimates," Marketing Science, INFORMS, vol. 21(2), pages 209-220, November.
- Bewley, Ronald & Fiebig, Denzil G., 1988. "A flexible logistic growth model with applications in telecommunications," International Journal of Forecasting, Elsevier, vol. 4(2), pages 177-192.
- Gruber, H. & Verboven, F.L., 1998.
"The Diffusion of Mobile Telecommunications Services in the European Union,"
1998-138, Tilburg University, Center for Economic Research.
- Gruber, Harald & Verboven, Frank, 2001. "The diffusion of mobile telecommunications services in the European Union," European Economic Review, Elsevier, vol. 45(3), pages 577-588, March.
- Gruber, Harald & Verboven, Frank, 1999. "The Diffusion of Mobile Telecommunications Services in the European Union," CEPR Discussion Papers 2054, C.E.P.R. Discussion Papers.
- Venkatesan, Rajkumar & Kumar, V., 2002. "A genetic algorithms approach to growth phase forecasting of wireless subscribers," International Journal of Forecasting, Elsevier, vol. 18(4), pages 625-646.
- Fildes, Robert & Kumar, V., 2002. "Telecommunications demand forecasting--a review," International Journal of Forecasting, Elsevier, vol. 18(4), pages 489-522.
- Geroski, Paul A, 1999.
"Models of Technology Diffusion,"
CEPR Discussion Papers
2146, C.E.P.R. Discussion Papers.
- Gruber, Harald, 2001. "Competition and innovation: The diffusion of mobile telecommunications in Central and Eastern Europe," Information Economics and Policy, Elsevier, vol. 13(1), pages 19-34, March.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If references are entirely missing, you can add them using this form.