Estimating Dynamic Models of Imperfect Competition

Patrick Bajari, C. Lanier Benkard and Jonathan Levin
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Patrick Bajari: U of Minnesota
C. Lanier Benkard: Stanford U

Research Papers from Stanford University, Graduate School of Business

Abstract: 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).

Date: 2007-04
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Related works:
Journal Article: Estimating Dynamic Models of Imperfect Competition (2007)
Working Paper: Estimating Dynamic Models of Imperfect Competition (2004)
Working Paper: Estimating Dynamic Models of Imperfect Competition (2004)
Working Paper: Estimating Dynamic Models of Imperfect Competition (2004)
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