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The FRBNY staff underlying inflation gauge: UIG

Listed author(s):
  • Amstad, Marlene

    (Federal Reserve Bank of New York)

  • Potter, Simon M.

    ()

    (Federal Reserve Bank of New York)

  • Rich, Robert W.

    ()

    (Federal Reserve Bank of New York)

Monetary policymakers and long-term investors would benefit greatly from a measure of underlying inflation that uses all relevant information, is available in real time, and forecasts inflation better than traditional underlying inflation measures such as core inflation measures. This paper presents the “FRBNY Staff Underlying Inflation Gauge (UIG)” for CPI and PCE. Using a dynamic factor model approach, the UIG is derived from a broad data set that extends beyond price series to include a wide range of nominal, real, and financial variables. It also considers the specific and time-varying persistence of individual subcomponents of an inflation series. An attractive feature of the UIG is that it can be updated on a daily basis, which allows for a close monitoring of changes in underlying inflation. This capability can be very useful when large and sudden economic fluctuations occur, as at the end of 2008. In addition, the UIG displays greater forecast accuracy than traditional measures of core inflation.

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Paper provided by Federal Reserve Bank of New York in its series Staff Reports with number 672.

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Length: 42 pages
Date of creation: 22 Apr 2014
Handle: RePEc:fip:fednsr:672
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  1. Amstad, Marlene & Fischer, Andreas M, 2004. "Sequential Information Flow and Real-Time Diagnosis of Swiss Inflation: Intra-Monthly DCF Estimates for a Low-Inflation Environment," CEPR Discussion Papers 4627, C.E.P.R. Discussion Papers.
  2. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
  3. Domenico Giannone & Lucrezia Reichlin & Luca Sala, 2005. "Monetary Policy in Real Time," NBER Chapters,in: NBER Macroeconomics Annual 2004, Volume 19, pages 161-224 National Bureau of Economic Research, Inc.
  4. Hogan, Seamus & Marianne Johnson & Thérèse Laflèche, 2001. "Core Inflation," Technical Reports 89, Bank of Canada.
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  6. Altissimo, Filippo & Mojon, Benoit & Zaffaroni, Paolo, 2009. "Can aggregation explain the persistence of inflation?," Journal of Monetary Economics, Elsevier, vol. 56(2), pages 231-241, March.
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  8. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
  9. Bernanke, Ben S. & Boivin, Jean, 2003. "Monetary policy in a data-rich environment," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 525-546, April.
  10. Cristadoro, Riccardo & Forni, Mario & Reichlin, Lucrezia & Veronese, Giovanni, 2001. "A Core Inflation Index for the Euro Area," CEPR Discussion Papers 3097, C.E.P.R. Discussion Papers.
  11. Amstad, Marlene & Fischer, Andreas M., 2010. "Monthly pass-through ratios," Journal of Economic Dynamics and Control, Elsevier, vol. 34(7), pages 1202-1213, July.
  12. William T. Gavin & Kevin L. Kliesen, 2008. "Forecasting inflation and output: comparing data-rich models with simple rules," Review, Federal Reserve Bank of St. Louis, issue May, pages 175-192.
  13. Bart Hobijn & Stefano Eusepi & Andrea Tambalotti, 2010. "The housing drag on core inflation," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue apr5.
  14. Marlene Amstad & Andreas M. Fischer, 2009. "Are Weekly Inflation Forecasts Informative?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(2), pages 237-252, April.
  15. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
  16. Stephen G. Cecchetti, 1995. "Inflation Indicators and Inflation Policy," NBER Chapters,in: NBER Macroeconomics Annual 1995, Volume 10, pages 189-236 National Bureau of Economic Research, Inc.
  17. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
  18. Gordon, Robert J, 1982. "Price Inertia and Policy Ineffectiveness in the United States, 1890-1980," Journal of Political Economy, University of Chicago Press, vol. 90(6), pages 1087-1117, December.
  19. Janine Aron & John Muellbauer, 2013. "New Methods for Forecasting Inflation, Applied to the US," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(5), pages 637-661, October.
  20. Brent Meyer & Guhan Venkatu & Saeed Zaman, 2013. "Forecasting inflation? Target the middle," Economic Commentary, Federal Reserve Bank of Cleveland, issue Apr.
  21. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
  22. Marlene Amstad & Andreas M. Fischer, 2009. "Do macroeconomic announcements move inflation forecasts?," Review, Federal Reserve Bank of St. Louis, issue Sep, pages 507-518.
  23. Domenico Giannone & Troy D. Matheson, 2007. "A New Core Inflation Indicator for New Zealand," International Journal of Central Banking, International Journal of Central Banking, vol. 3(4), pages 145-180, December.
  24. Marlene Amstad & Simon M. Potter, 2009. "Real time underlying inflation gauges for monetary policymakers," Staff Reports 420, Federal Reserve Bank of New York.
  25. James Dolmas, 2005. "Trimmed mean PCE inflation," Working Papers 0506, Federal Reserve Bank of Dallas.
  26. Richard Peach & Robert W. Rich & Anna Cororaton, 2011. "How does slack influence inflation?," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 17(June).
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