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FORECASTING INNOVATION DIFFUSION WITH NEAR-OPTIMAL BERTALANFFY-PÜTTER MODELS

Journal: INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGIES AND MANAGEMENT RESEARCH (Vol.7, No. 8)

Publication Date:

Authors : ; ; ;

Page : 1-11

Keywords : Akaike Weight; Bertalanffy-Pütter Differential; Equation; Least Squares; Near-Optimal Models; Forecasting; Model-Uncertainty;

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Abstract

Using a classical example for technology diffusion, the mechanization of agriculture in Spain since 1951, we considered the forecasting-intervals from the near-optimal Bertalanffy-Pütter (BP) models. We used BPmodels, as they considerably reduced the hitherto best fit (sum of squared errors) reported in literature. And we considered near-optimal models (their sum of squared errors is almost best), as they allowed to quantify model-uncertainty. This approach supplemented traditional sensitivity analyses (variation of model parameters), as for the present models and data even slight changes in the best-fit parameters resulted in very poorly fitting model curves.

Last modified: 2021-03-05 17:02:43