Dynamic oligopoly models are used in industrial organization toanalyze diverse dynamic phenomena. The applicability of these models has been severely limited, however, by the computational complexity involved in solving for the Markov perfect equilibrium (MPE). In previous work we introduced oblivious equilibrium (OE); a new solution concept for approximating MPE that dramatically reduces the computational complexity (Weintraub, Benkard, and Van Roy 2007, 2008). In this work we introduce several important extensions to OE.

First, in order to capture short-run transitional dynamics that may result, for example, from shocks or policy changes, we develop a nonstationary version of OE. Second, in order to capture the effects of industry-wide business cycles, we extend the definition of OE to accommodate models with aggregate random shocks. For both solution concepts we present algorithms for bounding approximation error and report results from computational case studies. Our results suggest that our methods greatly increase the set of dynamic oligopoly models that can be analyzed computationally.

(This is joint work with C. Lanier Benkard, Przemyslaw Jeziorski, and Benjamin Van Roy).