Long-Run Priors for Term Structure Models
39 Pages Posted: 22 Dec 2015
Date Written: December 2015
Dynamic no-arbitrage term structure models are popular tools for decomposing bond yields into expectations of future short-term interest rates and term premia. But there is insufficient information in the time series of observed yields to estimate the unconditional mean of yields in maximally flexible models. This can result in implausibly low estimates of long-term expected future short-term interest rates, as well as considerable uncertainty around those estimates. This paper proposes a tractable Bayesian approach for incorporating prior information about the unconditional means of yields. We apply it to UK data and find that with reasonable priors it results in more plausible estimates of the long-run average of yields, lower estimates of term premia in long-term bonds and substantially reduced uncertainty around these decompositions in both affine and shadow rate term structure models.
Keywords: Affine term structure model, shadow rate term structure model, Gibbs sampler
JEL Classification: C11, E43, G12
Suggested Citation: Suggested Citation