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

Author

Listed:
  • 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)

Suggested Citation

  • Alastair Cunningham & Chris Jeffery & George Kapetanios & Vincent Labhard, 2007. "A State Space Approach To The Policymaker's Data Uncertainty Problem," Money Macro and Finance (MMF) Research Group Conference 2006 168, Money Macro and Finance Research Group.
    • Handle: RePEc:mmf:mmfc06:168
    as

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    File URL: http://repec.org/mmf2006/up.10287.1159526154.pdf
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    References listed on IDEAS

    as
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    3. Sharon Kozicki, 2004. "How do data revisions affect the evaluation and conduct of monetary policy?," Economic Review, Federal Reserve Bank of Kansas City, vol. 89(Q I), pages 5-38.
    4. Martin D. D. Evans, 2005. "Where Are We Now? Real-Time Estimates of the Macroeconomy," International Journal of Central Banking, International Journal of Central Banking, vol. 1(2), September.
    5. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    6. 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.
    7. 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.
    8. 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.
    9. 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 568-572, August.
    10. George Kapetanios, 2004. "Estimating Time-Variation in Measurement Error from Data Revisions: An Application to Forecasting in Dynamic Models," Working Papers 520, Queen Mary University of London, School of Economics and Finance.
    11. Anthony Garratt & Shaun P Vahey, 2006. "UK Real-Time Macro Data Characteristics," Economic Journal, Royal Economic Society, vol. 116(509), pages 119-135, February.
    12. 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.
    13. 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(2‐3), pages 341-365, March.
    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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