FORECASTING INNOVATION DIFFUSION WITH NEAR-OPTIMAL BERTALANFFY-PÜTTER MODELS
Journal: INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGIES AND MANAGEMENT RESEARCH (Vol.7, No. 8)Publication Date: 2020-08-30
Authors : Manfred Kühleitner; Norbert Brunner; Katharina Renner-Martin;
Page : 1-11
Keywords : Akaike Weight; Bertalanffy-Pütter Differential; Equation; Least Squares; Near-Optimal Models; Forecasting; Model-Uncertainty;
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.
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