Simulated Maximum Likelihood Estimation Based on First-Order Conditions

49 Pages Posted: 27 Apr 2009

Date Written: 2006-08

Abstract

I describe a strategy for structural estimation that uses simulated maximum likelihood (SML) to estimate the structural parameters appearing in a model's first-order conditions (FOCs). Generalized method of moments (GMM) is often the preferred method for estimation of FOCs, as it avoids distributional assumptions on stochastic terms, provided all structural errors enter the FOCs additively, giving a single composite additive error. But SML has advantages over GMM in models where multiple structural errors enter the FOCs nonadditively. I develop new simulation algorithms required to implement SML based on FOCs, and I illustrate the method using a model of U.S. multinational corporations.

Suggested Citation

Keane, Michael P., Simulated Maximum Likelihood Estimation Based on First-Order Conditions (2006-08). International Economic Review, Vol. 50, Issue 2, pp. 627-675, May 2009, Available at SSRN: https://ssrn.com/abstract=1394005 or http://dx.doi.org/10.1111/j.1468-2354.2009.00543.x

Michael P. Keane (Contact Author)

University of New South Wales ( email )

Sydney, NSW
Australia

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