Estimating Dynamic Models of Imperfect Competition

40 Pages Posted: 11 May 2004 Last revised: 23 Jul 2010

See all articles by Patrick Bajari

Patrick Bajari

University of Michigan at Ann Arbor - Department of Economics; National Bureau of Economic Research (NBER)

C. Lanier Benkard

Stanford Graduate School of Business; National Bureau of Economic Research (NBER)

Jonathan Levin

Stanford University - Department of Economics; Stanford Graduate School of Business; National Bureau of Economic Research (NBER)

Multiple version iconThere are 2 versions of this paper

Date Written: April 2004

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 I.O. 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).

Suggested Citation

Bajari, Patrick and Benkard, C. Lanier and Levin, Jonathan D., Estimating Dynamic Models of Imperfect Competition (April 2004). NBER Working Paper No. w10450. Available at SSRN: https://ssrn.com/abstract=538342

Patrick Bajari (Contact Author)

University of Michigan at Ann Arbor - Department of Economics ( email )

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C. Lanier Benkard

Stanford Graduate School of Business ( email )

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National Bureau of Economic Research (NBER)

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Jonathan D. Levin

Stanford University - Department of Economics ( email )

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Stanford Graduate School of Business ( email )

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United States

National Bureau of Economic Research (NBER)

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United States

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