Determining market response functions by neural network modeling: A comparison to econometric techniques

The article starts by discussing basic concepts of artificial feedforward networks with hidden units. Backpropagation being the most wide-spread learning method to determine weights of connections between units is described. On the basis of data on a consumer brand, market response functions represented by artificial neural networks are compared to econometric models mainly on the basis of error measures. Economic interpretation of neural network results is given consideration. Finally advantages and limitations of neural networks are summarized.