Abstract: This paper studies the finite sample properties of the kernel
regression method of Boudoukh et al. (1998) for estimating
multifactor continuous-time term structure models. Monte Carlo
simulations are employed, with a grid-search technique to find the
optimal kernel bandwidth. The estimator exhibits truncation and
correlated residuals biases near the boundaries of the data. However,
the variance of the estimator is so high that the biases are unlikely
to be relevant from a hypothesis testing point of view. The
performance of the estimator is also studied under model
misspecification. Irrelevant regressors reduce efficiency and induce
additional biases in the estimates. Using Treasury bill data, I test
whether the estimates produced by the nonparametric estimator are
statistically distinguishable from estimates obtained under a
parametric model. The kernel regressions pick up nonlinearities in
the data that the parametric model cannot capture.
Keywords: Interest rate, multifactor, nonparametric
Full paper (509 KB PDF)
Home | FEDS | List of 1999 FEDS papers
Accessibility
To comment on this site, please fill out our feedback form.
Last update: January 10, 2000
|