The First Study, " Combining Revealed Preference Data with Stated Preference Data: a Latent Class

Revealing Additional Dimensions of Preference Heterogeneity in a Latent Class Mixed Multinomial Logit Model

Latent class models offer an alternative perspective to the popular mixed logit form, replacing the continuous distribution with a discrete distribution in which preference heterogeneity is captured by membership of distinct classes of utility description. Within each class, preference homogeneity is usually assumed (i.e., fixed parameters), although interactions with observed contextual effect...

On combining revealed and stated preferences to forecast customer behavior: three case studies

Many companies collect stated preference data (SP) like intentions and satisfaction as well as revealed preference data (RP) like actual purchasing behavior. It seems relevant to examine the predictive usefulness of this information for future revealed preferences, that is, customer behavior. In this paper we address this issue by considering three case studies.

Modelling preference heterogeneity in stated choice data: an analysis for public goods generated by agriculture

Stated choice models based on the random utility framework are becoming increasingly popular in the applied economics literature. The need to account for respondents’ preference heterogeneity in such models has motivated researchers in agricultural, environmental, health and transport economics to apply random parameter logit and latent class models. In most of the published literature these mo...

RURAL ECONOMY Combining Stated and Revealed Preference Data to Construct an Empirical Examination of Intrahousehold Bargaining

How to Estimate Scale-Adjusted Latent Class (SALC) Models and Obtain Better Segments with Discrete Choice Data

Introduction and Goal of this tutorial Researchers frequently use Latent Class (LC) choice models for strategic segmentation and targeting purposes to 1) find meaningful segments of respondents having different preferences, and 2) estimate part-worth utilities for these segments. However, LC models can form spurious segments (such as Class 4 in Figure 1 below) that differ primarily in terms of ...