Distinguishing between state dependence and unobserved heterogeneity in a discrete choice model

Abstract

This paper proposes a methodology to distinguish state dependence from unobserved heterogeneity in the estimation context of a discrete choice model using household scanner panel data. With traditional maximum-likelihood approaches, the individual effects become correlated with the covariates. In contrast, the proposed estimation method formalizes the correlation structure and thereby enables its consideration during the estimation process. In the resulting model, state dependence becomes a less significant variable, and the proposed model yields better measures of predictive performance than a conventional model. The proposed approach is illustrated through an empirical application in marketing.