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Author Hamilton, James D. ♦ Wu, Jing
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Gaussian Affine Term Structure Model ♦ Popular Canonical Representation ♦ Minimum-chi-square Estimation ♦ Separate Contribution ♦ Unidentified Region ♦ Numerical Optimization ♦ Global Maximum ♦ Small-sample Standard Error ♦ New Result ♦ Likelihood Function
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.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2012-01-01