Working Paper: NBER ID: w17772
Authors: James D. Hamilton; Jing Cynthia Wu
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: Gaussian Affine Term Structure Models; Identification; Estimation; Numerical Optimization
JEL Codes: C13; E43; G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| unidentified representations (Y40) | numerical difficulties (C60) |
| unidentified representations (Y40) | multiple parameter configurations (C39) |
| multiple parameter configurations (C39) | same likelihood function (C46) |
| MCSE method (C30) | clearer pathway to parameter estimation (C51) |
| MCSE method (C30) | mitigate numerical issues (C60) |
| mapping from structural parameters to reduced-form parameters (C51) | inference of parameters of interest (C20) |