Robust Predictions in Dynamic Screening

We characterize properties of optimal dynamic mechanisms using a variational approach that permits us to tackle directly the full program. This allows us to make predictions for a considerably broader class of stochastic processes than can be handled by the “first–order, Myersonian, approach,” which focuses on local incentive compatibility constraints and has become standard in the literature. Among other things, we characterize the dynamics of optimal allocations when the agent’s type evolves according to a stationary Markov processes, and show that, provided the players are sufficiently patient, optimal allocations converge to the efficient ones in the long run. JEL classification: D82