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Bootstrapping impulse responses in VAR analyses

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  • Lütkepohl, Helmut

Abstract

Because the parameters of vector autoregressive processes are often difficult to interpret directly, econometricians use quantities derived from the parameters to disentangle the relationships between the variables. Bootstrap methods are often used for inference on the derived quantities. Alternative bootstrap methods for this purpose are discussed, some related problems are pointed out and proposals are presented to overcome the difficulties at least partly. Some remaining problems are presented.

Suggested Citation

  • Lütkepohl, Helmut, 2000. "Bootstrapping impulse responses in VAR analyses," SFB 373 Discussion Papers 2000,22, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    • Handle: RePEc:zbw:sfb373:200022
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    Citations

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    Cited by:

    1. Myers, Robert J. & Johnson, Stanley R. & Helmar, Michael & Baumes, Harry, 2018. "Long-run and short-run relationships between oil prices, producer prices, and consumer prices: What can we learn from a permanent-transitory decomposition?," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 175-190.
    2. Constantinescu, Mihnea & Nguyen, Anh D.M., 2018. "Unemployment or credit: Which one holds the potential? Results for a small open economy with a low degree of financialization," Economic Systems, Elsevier, vol. 42(4), pages 649-664.
    3. Baum, Anja & Koester, Gerrit B., 2011. "The impact of fiscal policy on economic activity over the business cycle - evidence from a threshold VAR analysis," Discussion Paper Series 1: Economic Studies 2011,03, Deutsche Bundesbank.
    4. Buse, Rebekka & Schienle, Melanie & Urban, Jörg, 2022. "Assessing the impact of policy and regulation interventions in European sovereign credit risk networks: What worked best?," Journal of International Economics, Elsevier, vol. 139(C).
    5. Niklas Ahlgren & Paul Catani, 2017. "Wild bootstrap tests for autocorrelation in vector autoregressive models," Statistical Papers, Springer, vol. 58(4), pages 1189-1216, December.

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