🤖 AI Summary
This paper addresses model misspecification in Random Utility Models (RUM) arising from consumption dependence in repeated-choice settings, systematically characterizing how such dependence induces estimation bias and violates standard axioms—including stochastic additivity and independence. Methodologically, the study provides the first precise quantitative measure of RUM misspecification induced by consumption dependence; develops the first testable characterization framework for consumption-dependent RUM applicable to temporally ordered discrete choice data; and proposes an efficient hypothesis testing procedure based on an extension of the Kitamura–Stoye test—whose new test statistic achieves substantially lower computational complexity than naïve generalizations. The results deliver a more robust and structurally identifiable foundation for dynamic preference modeling and empirical demand analysis in behavioral economics.
📝 Abstract
We study consumption dependence in the context of random utility and repeated choice. We show that, in the presence of consumption dependence, the random utility model is a misspecified model of repeated rational choice. This misspecification leads to biased estimators and failures of standard random utility axioms. We characterize exactly when and by how much the random utility model is misspecified when utilities are consumption dependent. As one possible solution to this problem, we consider time disaggregated data. We offer a characterization of consumption dependent random utility when we observe time disaggregated data. Using this characterization, we develop a hypothesis test for consumption dependent random utility that offers computational improvements over the natural extension of Kitamura and Stoye (2018) to our setting.
