TY - JOUR
T1 - Technological forecasting with nonlinear models
AU - Lee, Jack C.
AU - Lu, Kevin W.
AU - Horng, S. Crystal
PY - 1992/4
Y1 - 1992/4
N2 - The 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.
AB - The 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.
KW - Logistic growth
KW - Nonlinear regression
KW - Technological forecasting
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U2 - 10.1002/for.3980110303
DO - 10.1002/for.3980110303
M3 - Article
AN - SCOPUS:84979339872
SN - 0277-6693
VL - 11
SP - 195
EP - 206
JO - Journal of Forecasting
JF - Journal of Forecasting
IS - 3
ER -