  Using a state-space system, I forecasted the US Treasury yields by employing
frequentist and Bayesian methods after first decomposing the yields of varying
maturities into its unobserved term structure factors. Then, I exploited the
structure of the state-space model to forecast the Treasury yields and compared
the forecast performance of each model using mean squared forecast error. Among
the frequentist methods, I applied the two-step Diebold-Li, two-step principal
components, and one-step Kalman filter approaches. Likewise, I imposed the five
different priors in Bayesian VARs: Diffuse, Minnesota, natural conjugate, the
independent normal inverse: Wishart, and the stochastic search variable
selection priors. After forecasting the Treasury yields for 9 different
forecast horizons, I found that the BVAR with Minnesota prior generally
minimizes the loss function. I augmented the above BVARs by including
macroeconomic variables and constructed impulse response functions with a
recursive ordering identification scheme. Finally, I fitted a sign-restricted
BVAR with dummy observations.
