Estimating Dynamic Games of Oligopolistic Competition: An Experimental Investigation
We evaluate dynamic oligopoly estimators with laboratory data. Using a stylized en-try/exit game, we estimate structural parameters under the assumption that the data are generated by a Markov-perfect equilibrium (MPE) and use the estimates to predict counterfactual behavior. The concern is that if the Markov assumption was violated one would mispredict counterfactual outcomes. The experimental method allows us to compare predicted behavior for counterfactuals to true counterfactuals implemented as treatments. Our main finding is that counterfactual prediction errors due to collusion are in most cases only modest in size.
For helpful discussions of this project we would like to thank John Asker, Isabelle Brocas, Colin Camerer, Juan Carrillo, Allan Collard-Wexler, Guillaume Frechette, Ali Hortacsu, Kei Kawai, Robin Lee, Alessandro Lizzeri, Ryan Oprea, Ariel Pakes, Tom Palfrey, Stephen Ryan, Andrew Schotter, Ralph Siebert, Matthew Shum, Anson Soderbery, Charles Sprenger, Severine Toussaert, Matan Tsur, Georg Weizsacker, Alistair Wilson, and Sevgi Yuksel. Vespa is grateful for financial support from the UCSB Academic Senate. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Tobias Salz & Emanuel Vespa, 2020. "Estimating dynamic games of oligopolistic competition: an experimental investigation," The RAND Journal of Economics, vol 51(2), pages 447-469.