Likelihood Inference in Non-Linear Term Structure Models: The Importance of the Lower Bound
35 Pages Posted: 22 Dec 2013
Date Written: December 20, 2013
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
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