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Identification and Estimation of Gaussian Affine Term Structure Models

James Hamilton () and Jing Cynthia Wu ()

No 17772, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C13 E43 G12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba and nep-ecm
Date: 2012-01
Note: AP ME
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Published as Hamilton, James D. & Wu, Jing Cynthia, 2012. "Identification and estimation of Gaussian affine term structure models," Journal of Econometrics, Elsevier, vol. 168(2), pages 315-331.

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