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The power of long-run structural VARs

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  • Christopher J. Gust
  • Robert J. Vigfusson

Abstract

Are structural vector autoregressions (VARs) useful for discriminating between macro models? Recent assessments of VARs have shown that these statistical methods have adequate size properties. In other words, in simulation exercises, VARs will only infrequently reject the true data generating process. However, in assessing a statistical test, we often also care about power: the ability of the test to reject a false hypothesis. Much less is known about the power of structural VARs. ; This paper attempts to fill in this gap by exploring the power of long-run structural VARs against a set of DSGE models that vary in degree from the true data generating process. We report results for two tests: the standard test of checking the sign on impact and a test of the shape of the response. For the models studied here, testing the shape is a more powerful test than simply looking at the sign of the response. In addition, relative to an alternative statistical test based on sample correlations, we find that the shape-based tests have greater power. Given the results on the power and size properties of long-run VARs, we conclude that these VARs are useful for discriminating between macro models.

Suggested Citation

  • Christopher J. Gust & Robert J. Vigfusson, 2009. "The power of long-run structural VARs," International Finance Discussion Papers 978, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgif:978
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    References listed on IDEAS

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    1. Kascha, Christian & Mertens, Karel, 2009. "Business cycle analysis and VARMA models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 267-282, February.
    2. Jordi Gali, 1999. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," American Economic Review, American Economic Association, vol. 89(1), pages 249-271, March.
    3. Erceg, Christopher J. & Henderson, Dale W. & Levin, Andrew T., 2000. "Optimal monetary policy with staggered wage and price contracts," Journal of Monetary Economics, Elsevier, vol. 46(2), pages 281-313, October.
    4. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    5. Elmar Mertens, 2008. "Are Spectral Estimators Useful for Implementing Long-Run Restrictions in SVARs?," Working Papers 08.01, Swiss National Bank, Study Center Gerzensee.
    6. Faust, Jon & Leeper, Eric M, 1997. "When Do Long-Run Identifying Restrictions Give Reliable Results?," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 345-353, July.
    7. Christiano, Lawrence J. & Vigfusson, Robert J., 2003. "Maximum likelihood in the frequency domain: the importance of time-to-plan," Journal of Monetary Economics, Elsevier, vol. 50(4), pages 789-815, May.
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    Cited by:

    1. Fujiwara, Ippei & Teranishi, Yuki, 2011. "Real exchange rate dynamics revisited: A case with financial market imperfections," Journal of International Money and Finance, Elsevier, vol. 30(7), pages 1562-1589.
    2. Lutz Kilian, 2013. "Structural vector autoregressions," Chapters,in: Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 22, pages 515-554 Edward Elgar Publishing.

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    Keywords

    Econometric models ; Sampling (Statistics);

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