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Testing identification via heteroskedasticity in structural vector autoregressive models

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  • Lütkepohl, Helmut
  • Meitz, Mika
  • Netšunajev, Aleksei
  • Saikkonen, Pentti

Abstract

Tests for identification through heteroskedasticity in structural vector autoregressive analysis are developed for models with two volatility states where the time point of volatility change is known. The tests are Wald-type tests for which only the unrestricted model, including the covariance matrices of the two volatility states, has to be estimated. The residuals of the model are assumed to be from the class of elliptical distributions, which includes Gaussian models. The asymptotic null distributions of the test statistics are derived, and simulations are used to explore their small-sample properties. Two empirical examples illustrate the usefulness of the tests in applied work.

Suggested Citation

  • Lütkepohl, Helmut & Meitz, Mika & Netšunajev, Aleksei & Saikkonen, Pentti, 2021. "Testing identification via heteroskedasticity in structural vector autoregressive models," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 24(1), pages 1-22.
    • Handle: RePEc:zbw:espost:233855
    DOI: 10.1093/ectj/utaa008
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    2. Lütkepohl, Helmut & Velinov, Anton, 2016. "Structural Vector Autoregressions : Checking Identifying Long-Run Restrictions via Heteroskedasticity," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 30, pages 377-392.
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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).
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    3. Christis Katsouris, 2023. "Structural Analysis of Vector Autoregressive Models," Papers 2312.06402, arXiv.org, revised Feb 2024.
    4. Matei Demetrescu & Robinson Kruse-Becher, 2021. "Is U.S. real output growth really non-normal? Testing distributional assumptions in time-varying location-scale models," CREATES Research Papers 2021-07, Department of Economics and Business Economics, Aarhus University.
    5. 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.
    6. Lukas Boer & Lukas Menkhoff & Malte Rieth, 2023. "The multifaceted impact of US trade policy on financial markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 388-406, April.
    7. Hu, Zhepeng & Huang, Joshua & Yan, Lei & Yuan, Jinghong, 2023. "Deconstructing Urea Fertilizer Price Spikes: The Role of Supply-Demand, Speculation, and Energy Prices," 2023 Annual Meeting, July 23-25, Washington D.C. 335529, Agricultural and Applied Economics Association.

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    More about this item

    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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