Over the past two decades, validation of choice models has focused on predictive validity rather than parameter bias. In real-world validation of choice models, true parameter values are unknown, so examination of parameter bias is not possible. In contrast, the main focus of this study is parameter bias in simulated scanner-panel choice data with known parameter values. Study of parameter bias enables the assessment of a fundamental issue not addressed in the choice modeling literature—the extent to which the logit choice model is capable of distinguishing unobserved effects that give rise to persistence in observed choices (e.g., heterogeneity and state dependence). Although econometric theory provides some information about the causes of bias, the extent of such bias in typical scanner data applications remains unclear. The authors present an extensive simulation study that provides information on the extent of bias resulting from the misspecification of four unobserved effects that receive frequent attention in the literature—choice set effects, heterogeneity in preferences and market response, state dependence, and serial correlation. The authors outline implications for model builders and managers. In general, the potential for parameter bias in choice model applications appears to be high. Overall, a logit model with choice set effects and the Guadagni–Little loyalty variable produces the most valid parameter estimates.

Managers often employ market response models as decision aids and historical information of competitors' market outcomes to aid their competitive decisions in oligopolistic settings. However, little is known about how access to a decision aid or the availability of competitors' market outcomes impact a firm's competitive decisions (e.g., prices) or market outcomes resulting from those decisions (e.g., profits), or how managers make these decisions across such informational conditions. Hence, the objective of this paper is twofold. First, we investigate whether access to a decision aid and historical information of competitors' outcomes yields more- or less-competitive decisions and outcomes. Second, we determine which learning constructs, such as choice reinforcement and beliefs about projected profits, best explain competitive actions across various information conditions. We find that relative to the availability of competitive information, access to a decision aid has a larger effect on lowering prices and profits. We also find that in two-firm markets, price competition is even more intense than in five-firm markets. Similarly, the availability of market share information leads to more aggressive pricing even when profits are held constant. Finally, we outline the implications of our findings in making managerial resource allocations to market research endeavors.