Valuing Consumer Preferences with the CUB Model: A CaseStudy of Fair Trade Coffee

D'Elia and Piccolo (2005) have recently proposed a mixture distribution, named CUB, for ordinal data. The use of such a mixture distribution for modelling ratings is justified by the following consideration: the judgment that a subject expresses is the result of two components, uncertainty and selectiveness. The possibility of relating the parameters of CUB models to covariates makes the formulation interesting for practical applicationsIn this case study, a sample of 224 fair-trade coffee consumers were interviewed at stores. With this data-set, CUB model split consumers, according to their preferences, in two different segments: one showing high price elasticity, and one with a low price elasticity. As regards the potential of the CUB model, it showed a considerable integration capacity with stochastic utility models, namely latent class models. Indeed, by using the segmentation factors emerging from the CUB as covariates of segmentation in a latent class model and setting the number of classes equal to those emerging from the CUB, it was possible to estimate a model which not only validated the findings of the CUB but also allowed estimation of the WTP for the fair trade characteristic in the different groups

Direct multi-step estimation and time series classification

The AR metric represents a consolidated model-based approach for time series classification. The goodness of the final classification may of course be affected by the misspecification of the models describing the observed time series. This article investigates whether a direct multi-step estimation approach can shed some more light on time series comparison

On the divergence between linear processes

In this paper we consider the problem of discriminating between stationary Gaussian processes. We examine the Kullback-Liebler divergence, which has been widely applied to classify time series with respect to the dominant frequency bands. Moreover, we analyse the connections between that approach based on the comparison of spectral densities and the alternative form in terms of the parameters or ARMA models. Finally, the relations between the J-divergence and other discrimination measures are discussed