✦ LIBER ✦

Technological forecasting with nonlinear models

✍ Scribed by Jack C. Lee; Kevin W. Lu; S. Crystal Horng

Publisher: John Wiley and Sons
Year: 1992
Tongue: English
Weight: 583 KB
Volume: 11
Category: Article
ISSN: 0277-6693
DOI: 10.1002/for.3980110303

No coin nor oath required. For personal study only.

✦ Synopsis

The S-shaped growth curves such as Gompertz, logistic, normal and Weibull 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.

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