A simple way to reduce estimation bias in some dynamic choice models

This study proposes a simple estimator for dynamic choice models with continuous unobserved heterogeneity, state dependence induced by a first-order Markov process, and tagged choice coefficients that are not brand specific. As opposed to the estimator most frequently used in marketing studies, which drops the first choice of each panelist, this estimator keeps that choice in the estimation sample but ignores the impact of presample choices on first observed choice. The proposed method presents major advantages over dropping the first observation: (1) it yields low bias estimates for state dependence coefficients and, thus, it can be used to assess the presence and the magnitude of state dependence; (2) it is informative about the magnitude of bias in its estimates; and (3) it yields substantially and significantly smaller biases. The results of this study can be used to determine if the proposed method is suitable for specific applications on real data.