Assessing the Effectiveness of Date-Based Forward Guidance at the Zero Lower Bound with a Non-Gaussian Affine Term-Structure Model

2014 ◽  
Author(s):  
T. K. Chung ◽  
Cho-Hoi Hui ◽  
Ka-Fai Li
Keyword(s):  
Lower Bound ◽  
Term Structure ◽  
Zero Lower Bound ◽  
Structure Model ◽  
Affine Term Structure ◽  
Term Structure Model ◽  
Forward Guidance ◽  
Non Gaussian ◽  
Affine Term Structure Model

scholarly journals Estimating the Term Structure with Linear Regressions: Getting to the Roots of the Problem

2019 ◽  
Author(s):  
Adam Goliński ◽  
Peter Spencer
Keyword(s):  
Term Structure ◽  
Eigenvalue Decomposition ◽  
Structure Model ◽  
Affine Term Structure ◽  
Term Structure Model ◽  
Linear Estimators ◽  
Self Consistent ◽  
Ml Estimator ◽  
Affine Term Structure Model ◽  
Linear Regressions

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.

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