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A State Space Approach To The Policymaker's Data Uncertainty Problem

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Author Info

  • Alastair Cunningham

    (Bank of England)

  • Chris Jeffery

    (Bank of England)

  • George Kapetanios

    (Queen Mary and WestÂ…eld College and Bank of England)

  • Vincent Labhard

    (European Central Bank)

Abstract

The paper describes the challenges that uncertainty over the true value of key macroeconomic variables poses for policymakers and the way in which they may form and update their priors in light of a range of indicators. Speci…cally, it casts the data uncertainty challenge in state space form and illustrates - in this setting - how the policymaker’s data uncertainty problem is related to any constraints that an optimising statistical agency might face in resolving its own data uncertainty challenge. The paper uses this intuition to motivate a set of identifying assumptions that might be used in the practical application of the Kalman Filter to form and update priors on the basis of a variety of indicators. In doing so, it moves beyond the simple methodology for deriving "best guesses" of the true value of economic variables outlined in Ashley, Driver, Hayes, and Je¤ery (2005)

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File URL: http://repec.org/mmf2006/up.10287.1159526154.pdf
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Bibliographic Info

Paper provided by Money Macro and Finance Research Group in its series Money Macro and Finance (MMF) Research Group Conference 2006 with number 168.

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Date of creation: 02 Feb 2007
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Handle: RePEc:mmf:mmfc06:168

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Web page: http://www.essex.ac.uk/afm/mmf/index.html

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References

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  1. Yates, Tony & Richard Harrison & George Kapetanios, 2003. "Forecasting with measurement errors in dynamic models," Royal Economic Society Annual Conference 2003 225, Royal Economic Society.
  2. Evans, Martin D, 2005. "Where Are We Now? Real-Time Estimates of the Macroeconomy," MPRA Paper 831, University Library of Munich, Germany.
  3. Weale, Martin, 1985. "Testing Linear Hypotheses on National Account Data," The Review of Economics and Statistics, MIT Press, vol. 67(4), pages 685-89, November.
  4. Anthony Garratt & Shaun P Vahey, 2006. "UK Real-Time Macro Data Characteristics," Economic Journal, Royal Economic Society, vol. 116(509), pages F119-F135, 02.
  5. 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.
  6. Orphanides, Athanasios, 2000. "The quest for prosperity without inflation," Working Paper Series 0015, European Central Bank.
  7. Anthony Garratt & Kevin Lee & Emi Mise & Kalvinder Shields, 2006. "Real Time Representations of the Output Gap," Birkbeck Working Papers in Economics and Finance 0619, Birkbeck, Department of Economics, Mathematics & Statistics.
  8. 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.
  9. Sargent, Thomas J, 1989. "Two Models of Measurements and the Investment Accelerator," Journal of Political Economy, University of Chicago Press, vol. 97(2), pages 251-87, April.
  10. Patterson, K. D., 1994. "A state space model for reducing the uncertainty associated with preliminary vintages of data with an application to aggregate consumption," Economics Letters, Elsevier, vol. 46(3), pages 215-222, November.
  11. Howrey, E Philip, 1978. "The Use of Preliminary Data in Econometric Forecasting," The Review of Economics and Statistics, MIT Press, vol. 60(2), pages 193-200, May.
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Cited by:
  1. Seth Pruitt, 2012. "Uncertainty Over Models and Data: The Rise and Fall of American Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44, pages 341-365, 03.
  2. Garratt, Anthony & Lee, Kevin & Mise, Emi & Shields, Kalvinder, 2009. "Real time representation of the UK output gap in the presence of model uncertainty," International Journal of Forecasting, Elsevier, vol. 25(1), pages 81-102.

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