Nonparametric econometric modelling: a neural network approach
Article Abstract:
The feasibility of applying neural networks to nonparametric econometric modelling is considered. The approach is demonstrated using real-life data on the prices and consumption of durables, non-durables and services in the US between 1932 and 1972. Its implementation, however, required the pre-processing of raw data, the imposition of constraints during the training stage, and cross-validation of the generated function.
Publication Name: European Journal of Operational Research
Subject: Business, international
ISSN: 0377-2217
Year: 1996
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Analyzing mathematical models with inductive learning networks
Article Abstract:
The viability of inductive learning networks to mathematical model analysis was investigated. Specifically, the group method of data handling and feedforward neural networks trained using the backpropagation technique were applied to analyze a facility location model. To this end, the key input and output variables and the relations between unknown model parameters and the corresponding objective functions were derived.
Publication Name: European Journal of Operational Research
Subject: Business, international
ISSN: 0377-2217
Year: 1996
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Parametric distance functions vs. nonparametric neural networks for estimating road travel distances
Article Abstract:
The viability of nonparametric neural networks for approximating road travel distances was investigated. Specifically, regression neural networks based on Gaussian kernels and backpropagation-trained multilayer perceptrons were compared to parametric distance functions in actual distance estimation studies using Turkish cities. The results showed that the neural networks delivered more accurate measurements.
Publication Name: European Journal of Operational Research
Subject: Business, international
ISSN: 0377-2217
Year: 1996
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