Authors: Eric Eisenstat
Addresses: Faculty of Business Administration, University of Bucharest, Blvd. Regina Elisabeta nr. 4 – 12, Sector 3, Bucharest 030018, Romania; RIMIR, Intrarea Stirbei Voda nr. 5, Sector 1, Bucharest 010124, Romania
Abstract: The focus of this paper is on developing a methodology for dealing with behavioural model uncertainty in structural oligopoly models. It is well recognised that being an essential part of the identification strategy, the particular choice of a behavioural model embodies a highly influential, yet largely arbitrary, set of assumptions in the structural framework. The methods developed here are founded in Bayesian model averaging techniques and provide a practically and conceptually desirable way of accommodating behavioural model uncertainty in structural estimation. Moreover, a substantial feature of this approach is that it yields straightforward model comparison through the model posterior distribution. These methods are applied to estimate the parameters of the industry demand curve and firms' cost functions in oligopoly markets (e.g., marginal costs, markups, etc.). Three models of oligopoly behaviour are considered: one non-cooperative and two variations of cooperative with unobserved demand shocks. The specific industry analysed is the 1800s railroad cartel, commonly known as the Joint Executive Committee, which is widely familiar to industrial organisations economists. The results indicate that the algorithm performs quite well in correctly identifying cooperative behaviour, in additional to offering a clear view of the way in which model averaging resolves conflicts in inference arising from competing behavioural models.
Keywords: structural estimation; set identification; multiple equilibria; common parameters; Bayesian model averaging; BMA; behavioural model uncertainty; structural oligopoly models; modelling; industry demand curve; cost functions; oligopoly markets; cooperative behaviour; cooperation.
International Journal of Mathematical Modelling and Numerical Optimisation, 2013 Vol.4 No.3, pp.252 - 281
Received: 19 Dec 2012
Accepted: 21 Jun 2013
Published online: 25 Sep 2013 *