Abstract

http://ssrn.com/abstract=1085394
 
 

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Constrained Optimization Approaches to Estimation of Structural Models


Che-Lin Su


University of Chicago Booth School of Business

Kenneth L. Judd


Stanford University - The Hoover Institution on War, Revolution and Peace; Center for Robust Decisionmaking on Climate & Energy Policy (RDCEP); National Bureau of Economic Research (NBER)

December 20, 2011

Econometrica Forthcoming

Abstract:     
Estimating structural models is often viewed as computationally difficult, an impression partly due to a focus on the nested fixed-point (NFXP) approach. We propose a new constrained optimization approach for structural estimation. We show that our approach and the NFXP algorithm solve the same estimation problem, and yield the same estimates. Computationally, our approach can have speed advantages because we do not repeatedly solve the structural equation at each guess of structural parameters. Monte Carlo experiments on the canonical Zurcher bus-repair model demonstrate that the constrained optimization approach can be significantly faster.

Number of Pages in PDF File: 21

Keywords: structural estimation, constrained optimization, dynamic discrete choice models

JEL Classification: C13, C61

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Date posted: February 13, 2008 ; Last revised: January 14, 2012

Suggested Citation

Su, Che-Lin and Judd, Kenneth L., Constrained Optimization Approaches to Estimation of Structural Models (December 20, 2011). Econometrica Forthcoming. Available at SSRN: http://ssrn.com/abstract=1085394 or http://dx.doi.org/10.2139/ssrn.1085394

Contact Information

Che-Lin Su (Contact Author)
University of Chicago Booth School of Business ( email )
5807 S. Woodlawn Avenue
Chicago, IL 60637
United States
Kenneth L. Judd
Stanford University - The Hoover Institution on War, Revolution and Peace ( email )
Stanford, CA 94305-6010
United States
Center for Robust Decisionmaking on Climate & Energy Policy (RDCEP) ( email )
5735 S. Ellis Street
Chicago, IL 60637
United States

National Bureau of Economic Research (NBER) ( email )
1050 Massachusetts Avenue
Cambridge, MA 02138
United States
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