Abstract
We evalute reference price models with regard to their ability to explain brand choices of individual households. Reference price models are of the adaptive expectations and extrapolative expectations types. Brand choice is analyzed by means of multinomial logit (MNL) models. We specify the deterministic utility component of MNL-Models as both conventional linear function and nonlinear function. Nonlinear utility is approximated by an appropriate neural network, a feedforward multilayer perceptron with sigmoid hidden units. Reference price models of the extrapolative expectation type formed by lagged prices and a time trend are superior to those of the adaptive expectation type for household scanner panel data. Improvements of posterior probabilities of choice models due to the inclusion of reference prices, losses and gains are greater if nonlinear utility choice models are used.
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