Long-Run Priors for Term Structure Models

39 Pages Posted: 22 Dec 2015

See all articles by Andrew Meldrum

Andrew Meldrum

Board of Governors of the Federal Reserve System

Matt Roberts-Sklar

Bank of England

Date Written: December 2015

Abstract

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

Meldrum, Andrew and Roberts-Sklar, Matt, Long-Run Priors for Term Structure Models (December 2015). Bank of England Working Paper No. 575, Available at SSRN: https://ssrn.com/abstract=2706467 or http://dx.doi.org/10.2139/ssrn.2706467

Andrew Meldrum (Contact Author)

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

Matt Roberts-Sklar

Bank of England ( email )

Threadneedle Street
London, EC2R 8AH
United Kingdom

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