Likelihood inference in non-linear term structure models: the importance of the lower bound

Martin Andreasen () and Andrew Meldrum
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Martin Andreasen: Aarhus University, Postal: Fuglesangs Allé 4, 8210 Aarhus V, Denmark

No 481, Bank of England working papers from Bank of England

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 (search for similar items in EconPapers)
JEL-codes: C01 C58 G12 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2013-12-20
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:boe:boeewp:0481

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