Estimating the term structure with linear regressions: Getting to the roots of the problem

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Estimating the term structure with linear regressions: Getting to the roots of the problem. / Golinski, Adam; Spencer, Peter.

In: Journal of Financial Econometrics, 03.12.2019, p. 1-25.

Research output: Contribution to journalArticle

Harvard

Golinski, A & Spencer, P 2019, 'Estimating the term structure with linear regressions: Getting to the roots of the problem', Journal of Financial Econometrics, pp. 1-25. https://doi.org/10.1093/jjfinec/nbz031

APA

Golinski, A., & Spencer, P. (2019). Estimating the term structure with linear regressions: Getting to the roots of the problem. Journal of Financial Econometrics, 1-25. https://doi.org/10.1093/jjfinec/nbz031

Vancouver

Golinski A, Spencer P. Estimating the term structure with linear regressions: Getting to the roots of the problem. Journal of Financial Econometrics. 2019 Dec 3;1-25. https://doi.org/10.1093/jjfinec/nbz031

Author

Golinski, Adam ; Spencer, Peter. / Estimating the term structure with linear regressions: Getting to the roots of the problem. In: Journal of Financial Econometrics. 2019 ; pp. 1-25.

Bibtex - Download

@article{bb1ae3ef0fb9443bb32f0cad63cf965f,
title = "Estimating the term structure with linear regressions: Getting to the roots of the problem",
abstract = "Linear estimators of the affine term structure model are inconsistent since they cannot reproduce the factors used in estimation. This is a serious handicap empirically, giving a worse fit than the conventional ML estimator that ensures consistency. We show that a simple self-consistent estimator can be constructed using the eigenvalue decomposition of a regression estimator. The remaining parameters of the model follow analytically. Estimates from this model are virtually indistinguishable from that of the ML estimator. We apply the method to estimate various models of U.S. Treasury yields. These exercises greatly extend the range of models that can be estimated.",
author = "Adam Golinski and Peter Spencer",
note = "The Author(s) 2019. Published by Oxford University Press. All rights reserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher{\textquoteright}s self-archiving policy. Further copying may not be permitted; contact the publisher for details.",
year = "2019",
month = dec,
day = "3",
doi = "10.1093/jjfinec/nbz031",
language = "English",
pages = "1--25",
journal = "Journal of Financial Econometrics",
issn = "1479-8409",
publisher = "Oxford: Oxford University Press",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Estimating the term structure with linear regressions: Getting to the roots of the problem

AU - Golinski, Adam

AU - Spencer, Peter

N1 - The Author(s) 2019. Published by Oxford University Press. All rights reserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

PY - 2019/12/3

Y1 - 2019/12/3

N2 - Linear estimators of the affine term structure model are inconsistent since they cannot reproduce the factors used in estimation. This is a serious handicap empirically, giving a worse fit than the conventional ML estimator that ensures consistency. We show that a simple self-consistent estimator can be constructed using the eigenvalue decomposition of a regression estimator. The remaining parameters of the model follow analytically. Estimates from this model are virtually indistinguishable from that of the ML estimator. We apply the method to estimate various models of U.S. Treasury yields. These exercises greatly extend the range of models that can be estimated.

AB - Linear estimators of the affine term structure model are inconsistent since they cannot reproduce the factors used in estimation. This is a serious handicap empirically, giving a worse fit than the conventional ML estimator that ensures consistency. We show that a simple self-consistent estimator can be constructed using the eigenvalue decomposition of a regression estimator. The remaining parameters of the model follow analytically. Estimates from this model are virtually indistinguishable from that of the ML estimator. We apply the method to estimate various models of U.S. Treasury yields. These exercises greatly extend the range of models that can be estimated.

U2 - 10.1093/jjfinec/nbz031

DO - 10.1093/jjfinec/nbz031

M3 - Article

SP - 1

EP - 25

JO - Journal of Financial Econometrics

JF - Journal of Financial Econometrics

SN - 1479-8409

ER -