Forecasting the Term Structure of Interest Rates Using Integrated Nested Laplace Approximations
AbstractThis article discuss the use of Bayesian methods for inference and forecasting in dynamic term structure models through Integrated Nested Laplace Approximations (INLA). This method of analytical approximations allows for 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, as the estimation in two stages. The results obtained in the estimation of the dynamic Nelson-Siegel model indicate that this methodology performs more accurate out-of-sample forecasts compared to the methods of two-stage estimation by OLS and also Bayesian estimation methods using MCMC. These analytical approaches also allow calculating efficiently measures of model selection such as generalized cross validation and marginal likelihood, that may be computationally prohibitive in MCMC estimations.
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Bibliographic InfoPaper provided by Economics Research Group, IBMEC Business School - Rio de Janeiro in its series IBMEC RJ Economics Discussion Papers with number 2011-01.
Date of creation: 14 Mar 2011
Date of revision:
Term Structure; Latent Factors; Bayesian Forecasting; Laplace Approximations;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-03-26 (All new papers)
- NEP-ECM-2011-03-26 (Econometrics)
- NEP-FOR-2011-03-26 (Forecasting)
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