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Identifying Shocks in Structural VAR models via heteroskedasticity: a Bayesian approach

Author

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  • Dmitry Kulikov

    ()

  • Aleksei Netsunajev

Abstract

This paper contributes to the literature on statistical identification of macroeconomic shocks by proposing a Bayesian VAR with time varying volatility of the residuals that depends on a hidden Markov process, referred to as an MS-SVAR. With sufficient statistical information in the data and certain identifying conditions on the variance�covariance structure of the innovations, distinct volatility regimes of the reduced form residuals allow all structural SVAR matrices and impulse response functions to be estimated without the need for conventional a priori identifying restrictions. We give mathematical identification conditions and propose a novel combination of the Gibbs sampler and a Bayesian clustering algorithm for the posterior inference on MS-SVAR parameters. The new methodology is applied to US macroeconomic data on output, inflation, real money and policy rates, where the effects of two real and two nominal shocks are clearly identified

Suggested Citation

  • Dmitry Kulikov & Aleksei Netsunajev, 2016. "Identifying Shocks in Structural VAR models via heteroskedasticity: a Bayesian approach," Bank of Estonia Working Papers wp2015-8, Bank of Estonia, revised 19 Feb 2016.
  • Handle: RePEc:eea:boewps:wp2015-8
    as

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    References listed on IDEAS

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    More about this item

    Keywords

    Markov switching models; Volatility regimes; Statistical identification; Bayesian inference; Clustering methods; SVAR analysis;

    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
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling

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