Publication Date: June 2017
Under dynamic random utility, an agent (or population of agents) solves a dynamic decision problem subject to evolving private information. We analyze the fully general and non-parametric model, axiomatically characterizing the implied dynamic stochastic choice behavior. A key new feature relative to static or i.i.d. versions of the model is that when private information displays serial correlation, choices appear history dependent: diﬀerent sequences of past choices reflect diﬀerent private information of the agent, and hence typically lead to diﬀerent distributions of current choices. Our axiomatization imposes discipline on the form of history dependence that can arise under arbitrary serial correlation. Dynamic stochastic choice data lets us distinguish central models that coincide in static domains, in particular private information in the form of utility shocks vs. learning, and to study inherently dynamic phenomena such as choice persistence. We relate our model to speciﬁcations of utility shocks widely used in empirical work, highlighting new modeling tradeoﬀs in the dynamic discrete choice literature. Finally, we extend our characterization to allow past consumption to directly aﬀect the agent’s utility process, accommodating models of habit formation and experimentation.
Supplement pages: 32
Dynamic stochastic choice, Random utility, History dependence, Serially correlated utilities, Consumption persistence, Learning
JEL Classification Codes: D81, D83, D90CFDP 2092R
See CFP: CFP1660