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Bayesian identification of structural vector autoregression models

Author

Listed:
  • Arefiev, Nikolay

    () (National Research University Higher School of Economics (NRU HSE), Moscow, Russian Federation)

  • Khabibullin, Ramis

    () (National Research University Higher School of Economics (NRU HSE), Moscow, Russian Federation)

Abstract

We propose a new method of Bayesian identification of a structural vector autoregression based on the Bayesian model averaging. As compared to the literature on Bayesian SVAR averaging, the proposed algorithm can identify not only recursive, but also cyclical models given that some conditions specified in the paper hold. Bayesian model selection is made within the set of distinguishable on data models. We use simulations to assess the performance of the algorithm. We also check sensitivity of the proposed algorithm with respect to true parameter values, number of observations, and with respect to the parameters of prior distribution.

Suggested Citation

  • Arefiev, Nikolay & Khabibullin, Ramis, 2018. "Bayesian identification of structural vector autoregression models," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 49, pages 115-142.
    • Handle: RePEc:ris:apltrx:0340
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    References listed on IDEAS

    as
    1. Helmut Lütkepohl & Anton Velinov, 2016. "Structural Vector Autoregressions: Checking Identifying Long-Run Restrictions Via Heteroskedasticity," Journal of Economic Surveys, Wiley Blackwell, vol. 30(2), pages 377-392, April.
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    5. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Bayesian Graphical Models for STructural Vector Autoregressive Processes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(2), pages 357-386, March.
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    11. Korobilis, D, 2017. "Forecasting with many predictors using message passing algorithms," Essex Finance Centre Working Papers 19565, University of Essex, Essex Business School.
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    More about this item

    Keywords

    SVAR; identification; Bayesian model averaging; Bayesian model selection;

    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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