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


  • Márcio Laurini

    (IBMEC Business School)

  • Luiz Koodi Hotta



This 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.

Suggested Citation

  • Márcio Laurini & Luiz Koodi Hotta, 2011. "Forecasting the Term Structure of Interest Rates Using Integrated Nested Laplace Approximations," IBMEC RJ Economics Discussion Papers 2011-01, Economics Research Group, IBMEC Business School - Rio de Janeiro.
  • Handle: RePEc:ibr:dpaper:2011-01

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    Cited by:

    1. Márcio Poletti Laurini & Armênio Westin Neto, 2014. "Arbitrage in the Term Structure of Interest Rates: a Bayesian Approach," International Econometric Review (IER), Econometric Research Association, vol. 6(2), pages 77-99, September.
    2. repec:erh:journl:v:6:y:2014:i:2:p:78-100 is not listed on IDEAS
    3. Márcio Poletti Laurini, 2017. "A continuous spatio-temporal model for house prices in the USA," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(1), pages 235-269, January.

    More about this item


    Term Structure; Latent Factors; Bayesian Forecasting; Laplace Approximations;

    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

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