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A population dependent diffusion model with a stochastic extension

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  • Michalakelis, C.
  • Sphicopoulos, T.
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    Abstract

    Diffusion 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.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0169207012000386
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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 28 (2012)
    Issue (Month): 3 ()
    Pages: 587-606

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    Handle: RePEc:eee:intfor:v:28:y:2012:i:3:p:587-606

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Innovation diffusion; High technology markets; Technology estimation and forecasting; ‘‘Population” diffusion model (PDM); Stochastic diffusion models (SDM);

    References

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    1. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    2. 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.
    3. 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.
    4. Gruber, H. & Verboven, F.L., 1998. "The Diffusion of Mobile Telecommunications Services in the European Union," Discussion Paper 1998-138, Tilburg University, Center for Economic Research.
    5. 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.
    6. Fildes, Robert & Kumar, V., 2002. "Telecommunications demand forecasting--a review," International Journal of Forecasting, Elsevier, vol. 18(4), pages 489-522.
    7. Geroski, Paul A, 1999. "Models of Technology Diffusion," CEPR Discussion Papers 2146, C.E.P.R. Discussion Papers.
    8. 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.
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