Forecasting the Term Structure of Interest Rates Using Integrated Nested Laplace Approximations

摘要

This article discusses the use of Bayesian methods for inference and forecasting in dynamic term structure models through integrated nested Laplace approximations (INLA). This method of analytical approximation allows accurate inferences for latent factors, parameters and forecasts in dynamic models with reduced computational cost. In the estimation of dynamic term structure models it also avoids some simplifications in the inference procedures, such as the inefficient two-step ordinary least squares (OLS) estimation. The results obtained in the estimation of the dynamic Nelson-Siegel model indicate that this method performs more accurate out-of-sample forecasts compared to the methods of two-stage estimation by OLS and also Bayesian estimation methods using Markov chain Monte Carlo (MCMC). These analytical approaches also allow efficient calculation of measures of model selection such as generalized crossvalidation and marginal likelihood, which may be computationally prohibitive in MCMC estimations.