Technological forecasting with nonlinear models

Journal of Forecasting - Tập 11 Số 3 - Trang 195-206 - 1992
Jack C. Lee1,2,3,4,5, Kevin W. Lu1,2,3,6,5, Shih‐Cheng Horng1,2,3,7,5
1Bellcore, Morristown and Livingston, NJ, U.S.A.
2Jack C. Lee, Bellcore, 445 South Street, Morristown, NJ 07962-1910, USA
3Kevin W. Lu, Bellcore, 445 South Street, Morristown, NJ 07962-1910, USA
4Member of Statistics and Econometrics Research Group in the Applied Research Area of Bellcore. He received a B.A. in Business from National Taiwan University, an M.A. in Economics from the University of Rochester and a Ph.D. in Statistics from SUNY at Buffalo. His areas of research include growth curves, forecasting and applications of Statistics.
5S. Crystal Horng, Bellcore, 290 Mt. Pleasant Ave., Livingston, NJ 07039
6Received the B.S. degree in control engineering from National Chiao Tung University, Taiwan, in 1979, and the M.S. and D.Sc. degrees in systems science and mathematics from Washington University, St. Louis, Missouri, in 1981 and 1984, respectively. He has been a Member of Technical Staff in Applied Research of Bellcore, Morristown, New Jersey since 1984. His research interests include technological forecasting and economic analysis for the communications network systems.
7Received her B.S. in Mathematics from National Taiwan University, M.A. in Mathematics from University of California, Los Angeles and Ph.D. in Mathematics from University of California, Los Angeles. She is currently a member of technical staff in the Customer Decision Analysis group of the Market Research and Analysis center at Bellcore in Livingston, NJ. Her primary research interests are statistical forecasting, demand analysis for telecommunications services and statistical computing.

Tóm tắt

AbstractThe S‐shaped growth curves such as Gompertz, logistic, normal and Weibuli are widely used for forecasting technological substitutions. A family of data‐based transformed (DBT) models, which are linear in the regression parameters, including the above‐mentioned four models as special cases has been shown to be quite useful for short‐term forecasts. This paper explores modeling the technology penetration data directly with assumed S‐shaped growth curves. The resulting models, which are nonlinear in the regression parameters, also incorporate proper dependence structure and power transformation. It appears that the nonlinear modeling is a viable alternative to the DBT and other conventional forecasting models in forecasting technological substitutions. Hence, an appropriate strategy is to consider the nonlinear modeling approaches as possible alternatives and use the data at hand to select, via pseudo‐cross‐validation, the best model for forecasting purposes.

Từ khóa


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