Constrained Optimization Approaches to Estimation of Structural Models

21 Pages Posted: 13 Feb 2008 Last revised: 14 Jan 2012

See all articles by Che-Lin Su

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)

Date Written: December 20, 2011

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.

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

JEL Classification: C13, C61

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: https://ssrn.com/abstract=1085394 or http://dx.doi.org/10.2139/ssrn.1085394

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

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
1,884
Abstract Views
8,059
Rank
14,356
PlumX Metrics