A New Linear Estimator for Gaussian Dynamic Term Structure Models

This 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 defi ned by the no-arbitrage conditions upon which these models are built. Further, we note that our estimator remains easy-to-compute and asymptotically efficient in a variety of situations in which other recently proposed approaches might lose their tractability. We provide an empirical application in the context of the Canadian bond market