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The power of long-run structural VARs

  • Christopher Gust
  • Robert Vigfusson

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.

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Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series International Finance Discussion Papers with number 978.

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Date of creation: 2009
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Handle: RePEc:fip:fedgif:978
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  1. Christopher A. Sims & Tao A. Zha, 1998. "Does monetary policy generate recessions?," Working Paper 98-12, Federal Reserve Bank of Atlanta.
  2. Lawrence J. Christiano & Martin Eichenbaum & Charles Evans, 2001. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," NBER Working Papers 8403, National Bureau of Economic Research, Inc.
  3. Christian Kascha & Karel Mertens, 2008. "Business cycle analysis and VARMA models," Working Paper 2008/05, Norges Bank.
  4. 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.
  5. Jon Faust & Eric M. Leeper, 1994. "When do long-run identifying restrictions give reliable results?," International Finance Discussion Papers 462, Board of Governors of the Federal Reserve System (U.S.).
  6. Jordi Gali, 1996. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations," NBER Working Papers 5721, National Bureau of Economic Research, Inc.
  7. Elmar Mertens, 2008. "Are Spectral Estimators Useful for Implementing Long-Run Restrictions in SVARs?," Working Papers 08.01, Swiss National Bank, Study Center Gerzensee.
  8. Lawrence J. Christiano & Robert J. Vigfusson, 2001. "Maximum likelihood in the frequency domain: the importance of time-to-plan," Working Paper 0106, Federal Reserve Bank of Cleveland.
  9. Gospodinov, Nikolay, 2010. "Inference in Nearly Nonstationary SVAR Models With Long-Run Identifying Restrictions," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 1-12.
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