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Bayesian Vector Autoregressions with Non-Gaussian Shocks

Author

Listed:
  • Ching-Wai (Jeremy) Chiu

    (Bank of England)

  • Haroon Mumtaz

    (Queen Mary University of London)

  • Gabor Pinter

    (Bank of England)

Abstract

This paper proposes a Bayesian Vector Autoregression where the orthogonalised shocks are assumed to be non-Gaussian. A Gibbs sampling algorithm is provided to approximate the poste-rior distribution of the model parameters. An application to a model of the yield curve suggests that there is ample evidence against the assumption of normal shocks. The proposed model provides notable improvements both in terms of in-sample �t and out of sample forecasting.

Suggested Citation

  • Ching-Wai (Jeremy) Chiu & Haroon Mumtaz & Gabor Pinter, 2016. "Bayesian Vector Autoregressions with Non-Gaussian Shocks," CReMFi Discussion Papers 5, CReMFi, School of Economics and Finance, QMUL.
    • Handle: RePEc:qmm:wpaper:5
    as

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    File URL: https://www.qmul.ac.uk/sef/media/econ/research/cremfi/2016/DP5.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Bayesian VAR; Non-Gaussian shocks; Density Forecasting;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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