Likelihood Inference in Non-Linear Term Structure Models: The Importance of the Lower Bound

35 Pages Posted: 22 Dec 2013

See all articles by Martin M. Andreasen

Martin M. Andreasen

Aarhus University; CREATES, Aarhus University

Andrew Meldrum

Board of Governors of the Federal Reserve System

Date Written: December 20, 2013

Abstract

This paper shows how to use adaptive particle filtering and Markov chain Monte Carlo methods to estimate quadratic term structure models (QTSMs) by likelihood inference. The procedure is applied to a quadratic model for the United States during the recent financial crisis. We find that this model provides a better statistical description of the data than a Gaussian affine term structure model. In addition, QTSMs account perfectly for the lower bound whereas Gaussian affine models frequently imply forecast distributions with negative interest rates. Such predictions appear during the recent financial crisis but also prior to the crisis.

Keywords: Adaptive particle filtering, Bayesian inference, Higher-order moments, PMCMC, Quadratic term structure models

JEL Classification: C1, C58, G12

Suggested Citation

Andreasen, Martin M. and Meldrum, Andrew, Likelihood Inference in Non-Linear Term Structure Models: The Importance of the Lower Bound (December 20, 2013). Bank of England Working Paper No. 481, Available at SSRN: https://ssrn.com/abstract=2370449 or http://dx.doi.org/10.2139/ssrn.2370449

Martin M. Andreasen

Aarhus University ( email )

Aarhus
Denmark

CREATES, Aarhus University ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

HOME PAGE: http://econ.au.dk/research/research-centres/creates/people/junior-fellows/martin-andreasen/

Andrew Meldrum (Contact Author)

Board of Governors of the Federal Reserve System ( email )

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

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