Extensions to Oblivious Equilibrium

In a recent paper, Weintraub, Benkard, and Van Roy (2007b) propose an approximation method for analyzing Ericson and Pakes (1995)-style dynamic models of imperfect competition. In that paper, we defined a new notion of equilibrium, oblivious equilibrium (henceforth, OE), in which each firm is assumed to make decisions based only on its own state and knowledge of the long run average industry state, but where firms ignore current information about competitors’ states. The great advantage of OE is that they are much easier to compute than are Markov perfect equilibria (henceforth, MPE). Moreover, we showed that an OE provides meaningful approximations of long-run Markov perfect dynamics of an industry with many firms if, alongside some technical requirements, the equilibrium distribution of firm states obeys a light-tail condition. To facilitate using OE in practice, in Weintraub, Benkard, and Van Roy (2007a) we provide a computational algorithm for solving for OE, and approximation bounds that can be computed to provide researchers with a numerical measure of how close OE is to MPE in their particular application. We also provided computational evidence supporting the conclusion that OE often yields good approximations of MPE behavior for industries like those that empirical researchers would like to study. While our computational results suggest that OE will be useful in many applications on its own, we believe that a major contribution of OE will be as a starting point with which to build even better approximations. As a matter of fact, in Weintraub, Benkard, and Van Roy (2007a) we extend our base model as well as algorithms for computing OE and error bounds to incorporate aggregate shocks common to all firms. Such an extension is important, for example, when analyzing the dynamic effects of industry-wide business cycles. In this note we introduce other important extensions to OE.