Identification and Estimation of Gaussian Affine Term Structure Models

69 Pages Posted: 10 Aug 2011 Last revised: 25 Sep 2013

See all articles by James D. Hamilton

James D. Hamilton

University of California at San Diego; National Bureau of Economic Research (NBER)

Jing Cynthia Wu

University of Notre Dame - Department of Economics; National Bureau of Economic Research (NBER)

Multiple version iconThere are 2 versions of this paper

Date Written: January 13, 2012

Abstract

This paper develops new results for identification and estimation of Gaussian affine term structure models. We establish that three popular canonical representations are unidentified, and demonstrate how unidentified regions can complicate numerical optimization. A separate contribution of the paper is the proposal of minimum-chi-square estimation as an alternative to MLE. We show that, although it is asymptotically equivalent to MLE, it can be much easier to compute. In some cases, MCSE allows researchers to recognize with certainty whether a given estimate represents a global maximum of the likelihood function and makes feasible the computation of small-sample standard errors.

Keywords: affine term structure models, identification, estimation, minimum-chi-square

JEL Classification: E43, C13, G12

Suggested Citation

Hamilton, James D. and Wu, Jing Cynthia, Identification and Estimation of Gaussian Affine Term Structure Models (January 13, 2012). Journal of Econometrics, Vol. 168 , No. 2, 2012; Chicago Booth Research Paper No. 13-71. Available at SSRN: https://ssrn.com/abstract=1907718 or http://dx.doi.org/10.2139/ssrn.1907718

James D. Hamilton

University of California at San Diego ( email )

9500 Gilman Drive
Mail code: 0508
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National Bureau of Economic Research (NBER)

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Cambridge, MA 02138
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Jing Cynthia Wu (Contact Author)

University of Notre Dame - Department of Economics ( email )

Notre Dame, IN 46556
United States

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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