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How do data revisions affect the evaluation and conduct of monetary policy?

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  • Sharon Kozicki

Abstract

Many economic data series are revised as more comprehensive information becomes available and as methodologies improve. Even the latest available data are subject to uncertainty, and at some point historical data may be replaced by more accurately measured observations. Because monetary policy decisions are made with an eye to the state of the economy, data uncertainty complicates the evaluation and conduct of monetary policy. ; Kozicki focuses on revisions to data that policymakers often examine when assessing monetary policy options. While other studies have looked at the impact of data revisions on monetary policy, this article is the first to examine the policy implications of revisions in two widely used benchmarks of resource utilization?the Congressional Budget Office (CBO) estimates of potential output and the natural rate of unemployment. The article is also the first to consider how data revisions affect policy decisions through changes in estimates of the equilibrium real rate of interest. ; Kozicki finds that revisions to data can lead to policy regret?instances when revised data may suggest alternative actions would have been preferable to those taken. Based on this finding and analysis in other studies, she recommends making policy less sensitive to economic indicators that are subject to large revisions.

Suggested Citation

  • 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.
  • Handle: RePEc:fip:fedker:y:2004:i:qi:p:5-38:n:v.89no.1
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    Cited by:

    1. Richard G. Anderson & Charles S. Gascon, 2009. "Estimating U.S. output growth with vintage data in a state-space framework," Review, Federal Reserve Bank of St. Louis, vol. 91(Jul), pages 349-370.
    2. Lavan Mahadeva & Alex Muscatelli, 2005. "National Accounts Revisions and Output Gap Estimates in a Model of Monetary Policy with Data Uncertainty," Discussion Papers 14, Monetary Policy Committee Unit, Bank of England.
    3. Fabrizio Zampolli & Andrew Blake, 2005. "Time Consistent Policy in Markov Switching Models," Money Macro and Finance (MMF) Research Group Conference 2005 2, Money Macro and Finance Research Group.
    4. Sharon Kozicki & P. Tinsley, 2006. "Minding the Gap: Central Bank Estimates of the Unemployment Natural Rate," Computational Economics, Springer;Society for Computational Economics, vol. 27(2), pages 295-327, May.
    5. Stefano Neri & Tiziano Ropele, 2012. "Imperfect Information, Real‐Time Data and Monetary Policy in the Euro Area," Economic Journal, Royal Economic Society, vol. 122(561), pages 651-674, June.
    6. Pierre Siklos, 2006. "What Can We Learn from Comprehensive Data Revisions for Forecasting Inflation: Some US Evidence," Working Papers eg0049, Wilfrid Laurier University, Department of Economics, revised 2006.
    7. Clark, Todd E. & Kozicki, Sharon, 2005. "Estimating equilibrium real interest rates in real time," The North American Journal of Economics and Finance, Elsevier, vol. 16(3), pages 395-413, December.
    8. Jo, Soojin & Karnizova, Lilia & Reza, Abeer, 2019. "Industry effects of oil price shocks: A re-examination," Energy Economics, Elsevier, vol. 82(C), pages 179-190.
    9. Bernhardsen, Tom & Eitrheim, Oyvind & Jore, Anne Sofie & Roisland, Oistein, 2005. "Real-time data for Norway: Challenges for monetary policy," The North American Journal of Economics and Finance, Elsevier, vol. 16(3), pages 333-349, December.
    10. Marcela Meirelles Aurelio, 2005. "Do we really know how inflation targeters set interest rates?," Research Working Paper RWP 05-02, Federal Reserve Bank of Kansas City.
    11. Chang, Yoosoon & Kwak, Boreum, 2017. "U.S. monetary-fiscal regime changes in the presence of endogenous feedback in policy rules," IWH Discussion Papers 15/2017, Halle Institute for Economic Research (IWH).
    12. Ásgeir Daníelsson, 2008. "Accuracy in forecasting macroeconomic variables in Iceland," Economics wp39, Department of Economics, Central bank of Iceland.
    13. Marc-André Gosselin & Temel Taskin, 2023. "What Can Earnings Calls Tell Us About the Output Gap and Inflation in Canada?," Discussion Papers 2023-13, Bank of Canada.
    14. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    15. 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.
    16. Givens, Gregory E. & Salemi, Michael K., 2015. "Inferring monetary policy objectives with a partially observed state," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 190-208.
    17. Felipe Morandé & Mauricio Tejada, 2008. "Sources of Uncertainty for Conducting Monetary Policy in Chile," Working Papers Central Bank of Chile 492, Central Bank of Chile.
    18. Andrew P Blake & Fabrizio Zampolli, 2006. "Optimal monetary policy in Markov-switching models with rational expectations agents," Bank of England working papers 298, Bank of England.
    19. Todd E. Clark & Taisuke Nakata, 2006. "The trend growth rate of employment : past, present, and future," Economic Review, Federal Reserve Bank of Kansas City, vol. 91(Q I), pages 43-85.
    20. Todd E. Clark & Michael W. McCracken, 2006. "Forecasting of small macroeconomic VARs in the presence of instabilities," Research Working Paper RWP 06-09, Federal Reserve Bank of Kansas City.
    21. Blake, Andrew P. & Zampolli, Fabrizio, 2011. "Optimal policy in Markov-switching rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 35(10), pages 1626-1651, October.

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