Estimating dynamic models of imperfect competition

• 成果类型: Article
• 署名作者: Bajari, Patrick; Benkard, C. Lanier; Levin, Jonathan
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities; National Bureau of Economic Research; Stanford University; Stanford University
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.1111/j.1468-0262.2007.00796.x
• 发表日期: 2007
• 页码: 1331-1370
• 关键词: discrete-choice; equilibrium; industry; inference; MARKET; games

摘要

We describe a two-step algorithm for estimating dynamic games under the assumption that behavior is consistent with Markov perfect equilibrium. In the first step, the policy functions and the law of motion for the state variables are estimated. In the second step, the remaining structural parameters are estimated using the optimality conditions for equilibrium. The second step estimator is a simple simulated minimum distance estimator. The algorithm applies to a broad class of models, including industry competition models with both discrete and continuous controls such as the Ericson and Pakes (1995) model. We test the algorithm on a class of dynamic discrete choice models with normally distributed errors and a class of dynamic oligopoly models similar to that of Pakes and McGuire (1994).