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Identification and Estimation of Gaussian Affine Term Structure Models

  • James D. Hamilton
  • Jing Cynthia Wu

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.

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File URL: http://www.nber.org/papers/w17772.pdf
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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 17772.

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Date of creation: Jan 2012
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Publication status: 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.
Handle: RePEc:nbr:nberwo:17772
Note: AP ME
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