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What Can We Learn from Comprehensive Data Revisions for Forecasting Inflation: Some US Evidence

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  • Pierre Siklos

    ()
    (Wilfrid Laurier University)

Abstract

The empirical properties of benchmark revisions to key U.S. macroeconomic aggregates are examined. News versus noise impact of revisions is interpreted via the cointegration property of successive benchmark revisions. Cointegration breaks in the last two years before a benchmark revision. Hence, we conclude that there is some information content in benchmark revisions. The last point is illustrated by reporting that inflation forecasts could be improved by the addition of a time series that reflects benchmark revisions to real GDP. Standard backward and forward-looking Phillips curves are used to explore the statistical significance of benchmark revisions.

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File URL: http://www.wlu.ca/documents/22950/CompDataRev_Dec_2006.pdf
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Bibliographic Info

Paper provided by Wilfrid Laurier University, Department of Economics in its series Working Papers with number eg0049.

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Length: 42
Date of creation: 2006
Date of revision: 2006
Handle: RePEc:wlu:wpaper:eg0049

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Keywords: cointegration breakdown; real time data; Phillips curve;

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References

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  1. Gregory, Allan W & Hansen, Bruce E, 1996. "Tests for Cointegration in Models with Regime and Trend Shifts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(3), pages 555-60, August.
  2. Golinelli, Roberto & Parigi, Giuseppe, 2005. "Short-Run Italian GDP Forecasting and Real-Time Data," CEPR Discussion Papers 5302, C.E.P.R. Discussion Papers.
  3. Joseph A. Ritter, 2000. "Feeding the national accounts," Review, Federal Reserve Bank of St. Louis, issue Mar, pages 11-20.
  4. Sharon Kozicki, 2004. "How do data revisions affect the evaluation and conduct of monetary policy?," Economic Review, Federal Reserve Bank of Kansas City, issue Q I, pages 5-38.
  5. Siklos,Pierre L., 2002. "The Changing Face of Central Banking," Cambridge Books, Cambridge University Press, number 9780521780254, October.
  6. Siklos, Pierre L. & Granger, Clive W.J., 1997. "Regime-Sensitive Cointegration With An Application To Interest-Rate Parity," Macroeconomic Dynamics, Cambridge University Press, vol. 1(03), pages 640-657, September.
  7. Dean Croushore & Charles L. Evans, 2000. "Data revisions and the identification of monetary policy shocks," Working Paper Series WP-00-26, Federal Reserve Bank of Chicago.
  8. Donald W.K. Andrews, 1990. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Cowles Foundation Discussion Papers 943, Cowles Foundation for Research in Economics, Yale University.
  9. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
  10. Hall, Alastair R., 2004. "Generalized Method of Moments," OUP Catalogue, Oxford University Press, number 9780198775201.
  11. Athanasios Orphanides & Simon van Norden, 2004. "The reliability of inflation forecasts based on output gap estimates in real time," Finance and Economics Discussion Series 2004-68, Board of Governors of the Federal Reserve System (U.S.).
  12. Donald W.K. Andrews & Jae-Young Kim, 2003. "End-of-Sample Cointegration Breakdown Tests," Cowles Foundation Discussion Papers 1404, Cowles Foundation for Research in Economics, Yale University.
  13. Anthony Garratt & Shaun P Vahey, 2005. "UK Real-Time Macro Data Characteristics," Birkbeck Working Papers in Economics and Finance 0502, Birkbeck, Department of Economics, Mathematics & Statistics.
  14. Ben S. Bernanke & Jean Boivin, 2001. "Monetary Policy in a Data-Rich Environment," NBER Working Papers 8379, National Bureau of Economic Research, Inc.
  15. David E. Runkle, 1998. "Revisionist history: how data revisions distort economic policy research," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall, pages 3-12.
  16. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-29, October.
  17. D. W. K. Andrews, 2003. "End-of-Sample Instability Tests," Econometrica, Econometric Society, vol. 71(6), pages 1661-1694, November.
  18. repec:cup:macdyn:v:1:y:1997:i:3:p:640-57 is not listed on IDEAS
  19. N. Gregory Mankiw & Matthew D. Shapiro, 1986. "News or Noise? An Analysis of GNP Revisions," NBER Working Papers 1939, National Bureau of Economic Research, Inc.
  20. Herrmann, Heinz & Orphanides, Athanasios & Siklos, Pierre L., 2005. "Real-time data and monetary policy," The North American Journal of Economics and Finance, Elsevier, vol. 16(3), pages 271-276, December.
  21. Henrik Hansen & Søren Johansen, 1999. "Some tests for parameter constancy in cointegrated VAR-models," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 306-333.
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Citations

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Cited by:
  1. Juan Manuel Julio, . "Modeling Data Revisions," Borradores de Economia 641, Banco de la Republica de Colombia.
  2. Carlo Altavilla & Matteo Ciccarelli, 2011. "Monetary Policy Analysis in Real-Time. Vintage combination from a real-time dataset," CSEF Working Papers 274, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
  3. Anthony Garratt & Gary Koop & Emi Mise & Shaun Vahey, 2008. "Real-time Prediction with UK Monetary Aggregates in the Presence of Model Uncertainty," Reserve Bank of New Zealand Discussion Paper Series DP2008/13, Reserve Bank of New Zealand.
  4. Clements, Michael P. & Beatriz Galvão, Ana, 2010. "First announcements and real economic activity," European Economic Review, Elsevier, vol. 54(6), pages 803-817, August.
  5. Jan Jacobs & Jan-Egbert Sturm, 2008. "The Information Content of KOF Indicators on Swiss Current Account Data Revisions," CESifo Working Paper Series 2370, CESifo Group Munich.

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