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Structural Vector Autoregressions with Smooth Transition in Variances: The Interaction between U.S. Monetary Policy and the Stock Market

Author

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  • Helmut Lütkepohl
  • Aleksei Netsunajev
Abstract
In structural vector autoregressive analysis identifying the shocks of interest via heteroskedasticity has become a standard tool. Unfortunately, the approaches currently used for modelling heteroskedasticity all have drawbacks. For instance, assuming known dates for variance changes is often unrealistic while more exible models based on GARCH or Markov switching residuals are difficult to handle from a statistical and computational point of view. Therefore we propose a modelbased on a smooth change in variance that is exible as well as relatively easy to estimate. The model is applied to a five-dimensional system of U.S. variables to explore the interaction between monetary policy and the stock market. It is found that previously used conventional identification schemes in this context are rejected by the data if heteroskedasticity is allowed for. Shocks identified via heteroskedasticity have a different economic interpretation than the shocks identified using conventional methods.

Suggested Citation

  • Helmut Lütkepohl & Aleksei Netsunajev, 2014. "Structural Vector Autoregressions with Smooth Transition in Variances: The Interaction between U.S. Monetary Policy and the Stock Market," Discussion Papers of DIW Berlin 1388, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp1388
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Dominik Bertsche & Robin Braun, 2022. "Identification of Structural Vector Autoregressions by Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 328-341, January.
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    3. Lütkepohl, Helmut & Netšunajev, Aleksei, 2017. "Structural vector autoregressions with heteroskedasticity: A review of different volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 2-18.
    4. Pierre Guérin & Danilo Leiva-Leon, 2017. "Monetary policy, stock market and sectoral comovement," Working Papers 1731, Banco de España.
    5. Benjamin Beckers & Kerstin Bernoth, 2016. "Monetary Policy and Mispricing in Stock Markets," Discussion Papers of DIW Berlin 1605, DIW Berlin, German Institute for Economic Research.
    6. Bernoth, Kerstin & Herwartz, Helmut, 2021. "Exchange rates, foreign currency exposure and sovereign risk," Journal of International Money and Finance, Elsevier, vol. 117(C).
    7. Lanne, Markku & Meitz, Mika & Saikkonen, Pentti, 2017. "Identification and estimation of non-Gaussian structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 196(2), pages 288-304.
    8. Guay, Alain, 2021. "Identification of structural vector autoregressions through higher unconditional moments," Journal of Econometrics, Elsevier, vol. 225(1), pages 27-46.

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    More about this item

    Keywords

    Structural vector autoregressions; heteroskedasticity; smooth transition VAR models; identification via heteroskedasticity;
    All these keywords.

    JEL classification:

    • 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

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