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Choosing Between Different Time-Varying Volatility Models for Structural Vector Autoregressive Analysis

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  • Lütkepohl, Helmut
  • Schlaak, Thore

Abstract

The performance of information criteria and tests for residual heteroscedasticity for choosing between different models for time-varying volatility in the context of structural vector autoregressive analysis is investigated. Although it can be difficult to find the true volatility model with the selection criteria, using them is recommended because they can reduce the mean squared error of impulse response estimates substantially relative to a model that is chosen arbitrarily based on the personal preferences of a researcher. Heteroscedasticity tests are found to be useful tools for deciding whether time-varying volatility is present but do not discriminate well between different types of volatility changes. The selection methods are illustrated by specifying a model for the global market for crude oil.

Suggested Citation

  • Lütkepohl, Helmut & Schlaak, Thore, 2018. "Choosing Between Different Time-Varying Volatility Models for Structural Vector Autoregressive Analysis," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue 4, pages 715-735.
    • Handle: RePEc:zbw:espost:222929
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    Cited by:

    1. Fritsche, Jan Philipp & Klein, Mathias & Rieth, Malte, 2021. "Government spending multipliers in (un)certain times," Journal of Public Economics, Elsevier, vol. 203(C).
    2. Lütkepohl, Helmut & Schlaak, Thore, 2019. "Bootstrapping impulse responses of structural vector autoregressive models identified through GARCH," Journal of Economic Dynamics and Control, Elsevier, vol. 101(C), pages 41-61.
    3. Helmut Lütkepohl & Thore Schlaak, 2022. "Heteroscedastic Proxy Vector Autoregressions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1268-1281, June.
    4. Herwartz, Helmut & Lange, Alexander & Maxand, Simone, 2019. "Statistical identification in SVARs - Monte Carlo experiments and a comparative assessment of the role of economic uncertainties for the US business cycle," University of Göttingen Working Papers in Economics 375, University of Goettingen, Department of Economics.
    5. Demetrescu, Matei & Salish, Nazarii, 2024. "(Structural) VAR models with ignored changes in mean and volatility," International Journal of Forecasting, Elsevier, vol. 40(2), pages 840-854.
    6. Guay, Alain, 2021. "Identification of structural vector autoregressions through higher unconditional moments," Journal of Econometrics, Elsevier, vol. 225(1), pages 27-46.
    7. 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.
    8. Helmut Herwartz & Alexander Lange & Simone Maxand, 2022. "Data‐driven identification in SVARs—When and how can statistical characteristics be used to unravel causal relationships?," Economic Inquiry, Western Economic Association International, vol. 60(2), pages 668-693, April.
    9. Sultan Mehmood & Avner Seror, 2019. "The Political Economy of Foreign Aid and Growth:Theory and Evidence," Working Papers 19-10, Chapman University, Economic Science Institute.
    10. Karamysheva, Madina & Skrobotov, Anton, 2022. "Do we reject restrictions identifying fiscal shocks? identification based on non-Gaussian innovations," Journal of Economic Dynamics and Control, Elsevier, vol. 138(C).
    11. Herwartz, Helmut & Rohloff, Hannes & Wang, Shu, 2020. "Proxy SVAR identification of monetary policy shocks: MonteCarlo evidence and insights for the US," University of Göttingen Working Papers in Economics 404, University of Goettingen, Department of Economics.
    12. Udi Joshua & Hasan Güngör & Festus Victor Bekun, 2023. "Assessment of Foreign Direct Investment-Led Growth Argument in South Africa Amidst Urbanization and Industrialization: Evidence from Innovation Accounting Tests," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 14(3), pages 3374-3394, September.
    13. Herwartz, Helmut & Rohloff, Hannes & Wang, Shu, 2022. "Proxy SVAR identification of monetary policy shocks - Monte Carlo evidence and insights for the US," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    14. Martin Bruns & Helmut Lütkepohl, 2023. "An Alternative Bootstrap for Proxy Vector Autoregressions," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1857-1882, December.

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    Keywords

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    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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