A practitioner's guide to Bayesian estimation of discrete choice dynamic programming models

This paper provides a step-by-step guide to estimating infinite horizon discrete choice dynamic programming (DDP) models using a new Bayesian estimation algorithm (Imai et al., Econometrica 77:1865–1899, 2009a) (IJC). In the conventional nested fixed point algorithm, most of the information obtained in the past iterations remains unused in the current iteration. In contrast, the IJC algorithm extensively uses the computational results obtained from the past iterations to help solve the DDP model at the current iterated parameter values. Consequently, it has the potential to significantly alleviate the computational burden of estimating DDP models. To illustrate this new estimation method, we use a simple dynamic store choice model where stores offer “frequent-buyer” type rewards programs. Our Monte Carlo results demonstrate that the IJC method is able to recover the true parameter values of this model quite precisely. We also show that the IJC method could reduce the estimation time significantly when estimating DDP models with unobserved heterogeneity, especially when the discount factor is close to 1

Opportunity Cost, Inattention and the Bidder's Curse

Recent research suggests that auction winners sometimes fall prey to a “bidder’s curse”, paying more for an item at auction than they would have paid at a posted price. One explanation for this phenomenon is that bidders are inattentive to posted prices. We develop a model in which bidders’ inattention, and subsequent overbidding, is driven by a rational response to the opportunity cost of acquiring information about the posted price. We test our model in a laboratory experiment in which subjects bid in an auction while facing an opportunity cost of looking up the posted price. We vary the opportunity cost, and we show that information acquisition decreases and consequently overbidding increases with opportunity cost as predicted