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Forecasting wireless communication technologies


  • Sabrina Patino
  • Jisun Kim
  • Tugrul U. Daim


The purpose of the paper is to present a formal comparison of a variety of multiple regression models in technology forecasting for wireless communication. We compare results obtained from multiple regression models to determine whether they provide a superior fitting and forecasting performance. Both techniques predict the year of wireless communication technology introduction from the first (1G) to fourth (4G) generations. This paper intends to identify the key parameters impacting the growth of wireless communications. The comparison of technology forecasting approaches benefits future researchers and practitioners when developing a prediction of future wireless communication technologies. The items of focus will be to understand the relationship between variable selection and model fit. Because the forecasting error was successfully reduced from previous approaches, the quadratic regression methodology is applied to the forecasting of future technology commercialisation. In this study, the data will show that the quadratic regression forecasting technique provides a better fit to the curve.

Suggested Citation

  • Sabrina Patino & Jisun Kim & Tugrul U. Daim, 2010. "Forecasting wireless communication technologies," International Journal of Applied Management Science, Inderscience Enterprises Ltd, vol. 2(2), pages 169-197.
  • Handle: RePEc:ids:injams:v:2:y:2010:i:2:p:169-197

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    References listed on IDEAS

    1. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    2. Halkos, George & Salamouris, Dimitrios, 2001. "Efficiency Measures of the Greek Banking Sector: A Non-Parametric Approach for the Period 1997-1999," MPRA Paper 2858, University Library of Munich, Germany.
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