SIMULATED MAXIMUM LIKELIHOOD ESTIMATION BASED ON FIRST‐ORDER CONDITIONS* |
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| Authors: | Michael P Keane |
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| Institution: | 1. University of Technology Sydney, Australia;2. The statistical analysis of the confidential firm‐level data on U.S. multinational corporations reported in this study was conducted at the International Investment Division, Bureau of Economic Analysis, U.S. Department of Commerce, under arrangements that maintained legal confidentiality requirements. Seminar participants at Princeton and the University of Minnesota provided useful comments. Conversations with Penny Goldberg, Lee Ohanian, Tom Holmes, Sam Kortum, and Chris Sims were also very helpful. All remaining errors are of course my own. Please address correspondence to: Michael P. Keane, WP Carey School of Business, Arizona State University, P.O. Box 873806, Tempe, AZ 85287. Phone: 480‐965‐3531. E‐mail: . |
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| 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. |
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