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On Identifying Structural VAR Models via ARCH Effects

Author

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  • Milunovich George

    (Macquarie University, North Ryde, NSW, 2109, Australia)

  • Yang Minxian

    (School of Economics, University of New South Wales, Kensington, NSW, 2052, Australia)

Abstract

We consider the local identification of parameters in structural VAR models with ARCH type errors. By establishing a mapping between the structural and reduced-form models, we provide a set of sufficient conditions for the joint identification of all parameters. Under these conditions, as the structural parameters are identified, various restrictions on the parameters can be tested in a standard manner. For example, the significance test for the ARCH effect in the usual GARCH formulation for a structural shock does not suffer the complications caused by a lack of identification encountered in univariate GARCH models.

Suggested Citation

  • Milunovich George & Yang Minxian, 2013. "On Identifying Structural VAR Models via ARCH Effects," Journal of Time Series Econometrics, De Gruyter, vol. 5(2), pages 117-131, May.
    • Handle: RePEc:bpj:jtsmet:v:5:y:2013:i:2:p:117-131:n:5
    DOI: 10.1515/jtse-2013-0010
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    References listed on IDEAS

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    1. Arthur Lewbel, 2012. "Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 67-80.
    2. Dungey, Mardi & Milunovich, George & Thorp, Susan, 2010. "Unobservable shocks as carriers of contagion," Journal of Banking & Finance, Elsevier, vol. 34(5), pages 1008-1021, May.
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    4. Normandin, Michel & Phaneuf, Louis, 2004. "Monetary policy shocks:: Testing identification conditions under time-varying conditional volatility," Journal of Monetary Economics, Elsevier, vol. 51(6), pages 1217-1243, September.
    5. Todd Prono, 2008. "GARCH-based identification and estimation of triangular systems," Supervisory Research and Analysis Working Papers QAU08-4, Federal Reserve Bank of Boston.
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    Citations

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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.
    2. Vaqar Ahmed & Muhammad Zeshan, 2014. "Decomposing Change in Energy Consumption of the Agricultural Sector in Pakistan," Agrarian South: Journal of Political Economy, Centre for Agrarian Research and Education for South, vol. 3(3), pages 369-402, December.
    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. Dungey, Mardi & Milunovich, George & Thorp, Susan & Yang, Minxian, 2015. "Endogenous crisis dating and contagion using smooth transition structural GARCH," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 71-79.
    5. 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.
    6. Helmut Lütkepohl & Aleksei Netšunajev, 2015. "Structural Vector Autoregressions with Heteroskedasticity - A Comparison of Different Volatility Models," CESifo Working Paper Series 5308, CESifo.
    7. Daniel J Lewis, 2021. "Identifying Shocks via Time-Varying Volatility [First Order Autoregressive Processes and Strong Mixing]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(6), pages 3086-3124.
    8. 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.
    9. 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.
    10. Helmut Lütkepohl & Aleksei Netšunajev, 2015. "Structural Vector Autoregressions with Heteroskedasticy," SFB 649 Discussion Papers SFB649DP2015-015, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    11. Ruben Hipp, 2020. "On Causal Networks of Financial Firms: Structural Identification via Non-parametric Heteroskedasticity," Staff Working Papers 20-42, Bank of Canada.
    12. Daniel Lewis, 2024. "Identification based on higher moments," CeMMAP working papers 03/24, Institute for Fiscal Studies.
    13. Helmut Lütkepohl & George Milunovich, 2015. "Testing for Identification in SVAR-GARCH Models: Reconsidering the Impact of Monetary Shocks on Exchange Rates," Discussion Papers of DIW Berlin 1455, DIW Berlin, German Institute for Economic Research.
    14. 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.
    15. André L P Ribeiro & Luiz K Hotta, 2016. "Estimation of the Heteroskedastic Canonical Contagion Model with Instrumental Variables," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-13, December.
    16. Lütkepohl, Helmut & Milunovich, George, 2016. "Testing for identification in SVAR-GARCH models," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 241-258.

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