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Vector Autoregressions and Reality

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  • Runkle, David E

Abstract

This article questions the statistical significance of variance decompositions and impulse response functions for unrestricted vector auto regressions. It suggests that previous authors have failed to provide confidence intervals for variance decompositions and impulse response functions. Two methods of computing such confidence intervals are developed: first, using a normal approximation; second, using bootstrapped resampling. An example from Sims's work is used to illustrate the importance of computing these confidence intervals. In this example, the 95% confidence intervals for variance decompositions span up to 66 percentage points at the usual forecasting horizon.

Suggested Citation

  • Runkle, David E, 2002. "Vector Autoregressions and Reality," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 128-133, January.
    • Handle: RePEc:bes:jnlbes:v:20:y:2002:i:1:p:128-33
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    Cited by:

    1. YUAN, Chunming & CHEN, Ruo, 2015. "Policy transmissions, external imbalances, and their impacts: Cross-country evidence from BRICS," China Economic Review, Elsevier, vol. 33(C), pages 1-24.
    2. Ke Li & Boqiang Lin & Xiying Liu, 2015. "Special: Theme of Clean Coal How Policy Strategies Affect Clean Coal Technology Innovation in China? A Patent-Based Approach," Energy & Environment, , vol. 26(6-7), pages 1015-1033, November.
    3. Nikki Panjer & Leo Haan & Jan P. A. M. Jacobs, 2020. "Is fiscal policy in the euro area Ricardian?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 47(2), pages 411-429, May.
    4. Mohammad Reza Farzanegan, 2014. "Military Spending and Economic Growth: The Case of Iran," Defence and Peace Economics, Taylor & Francis Journals, vol. 25(3), pages 247-269, June.

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