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

  • 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)

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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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
Contact details of provider: Web page: http://www.essex.ac.uk/afm/mmf/index.html

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  1. Harrison, Richard & Kapetanios, George & Yates, Tony, 2005. "Forecasting with measurement errors in dynamic models," International Journal of Forecasting, Elsevier, vol. 21(3), pages 595-607.
  2. Anthony Garratt & Kevin Lee & Emi Mise & Kalvinder Shields, 2008. "Real-Time Representations of the Output Gap," The Review of Economics and Statistics, MIT Press, vol. 90(4), pages 792-804, November.
  3. George Kapetanios & Tony Yates, 2004. "Estimating time-variation in measurement error from data revisions; an application to forecasting in dynamic models," Bank of England working papers 238, Bank of England.
  4. 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.
  5. Orphanides, Athanasios, 2003. "The quest for prosperity without inflation," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 633-663, April.
  6. Anthony Garratt & Shaun P Vahey, 2006. "UK Real-Time Macro Data Characteristics," Economic Journal, Royal Economic Society, vol. 116(509), pages F119-F135, 02.
  7. 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.
  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. Martin D. D. Evans(Georgetown University and NBER), 2005. "Where Are We Now? Real-time Estimates of the Macro Economy," Working Papers gueconwpa~05-05-02, Georgetown University, Department of Economics.
  10. 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.
  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.
  12. Doran, Howard E, 1992. "Constraining Kalman Filter and Smoothing Estimates to Satisfy Time-Varying Restrictions," The Review of Economics and Statistics, MIT Press, vol. 74(3), pages 568-72, August.
  13. 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.
  14. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543, March.
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