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

42 Pages Posted: 17 Jan 2011

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: January 11, 2011

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 quadratic models for the US and UK during the recent financial crisis. We find that these models provide a better statistical description of the data than Gaussian affine term structure models. In addition, QTSMs account perfectly for the zero lower bound whereas Gaussian affine models frequently imply forecast distributions with negative interest rates. Such predictions appear during the recent financial crisis in the US and UK 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 Zero Lower Bound (January 11, 2011). Available at SSRN: https://ssrn.com/abstract=1738206 or http://dx.doi.org/10.2139/ssrn.1738206

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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