A New Linear Estimator for Gaussian Dynamic Term Structure Models
AbstractThis paper proposes a novel regression-based approach to the estimation of Gaussian dynamic term structure models that avoids numerical optimization. This new estimator is an asymptotic least squares estimator defined by the no-arbitrage conditions upon which these models are built. We discuss some efficiency considerations of this estimator, and show that it is asymptotically equivalent to maximum likelihood estimation. Further, we note that our estimator remains easy-to-compute and asymptotically efficient in a variety of situations in which other recently proposed approaches lose their tractability. We provide an empirical application in the context of the Canadian bond market.
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Bibliographic InfoPaper provided by Bank of Canada in its series Working Papers with number 13-10.
Length: 63 pages
Date of creation: 2013
Date of revision:
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Asset Pricing; Econometric and statistical methods; Interest rates;
Find related papers by JEL classification:
- E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-05-05 (All new papers)
- NEP-ECM-2013-05-05 (Econometrics)
- NEP-IAS-2013-05-05 (Insurance Economics)
- NEP-MAC-2013-05-05 (Macroeconomics)
- NEP-ORE-2013-05-05 (Operations Research)
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