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In search of a robust inflation forecast

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Author Info

  • Scott Brave
  • Jonas D. M. Fisher

Abstract

It is difficult to consistently improve upon forecasts of inflation based solely on the most recent data on inflation. In this article, we show how to do so. Our main finding is that the most robust forecasts combine information from several different forecasting models, each of which incorporates the information in the available inflation indicators in different ways.

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File URL: http://www.chicagofed.org/digital_assets/publications/economic_perspectives/2004/ep_4qtr2004_part2_Brave_Fisher.pdf
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Bibliographic Info

Article provided by Federal Reserve Bank of Chicago in its journal Economic Perspectives.

Volume (Year): (2004)
Issue (Month): Q IV ()
Pages: 12-31

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Handle: RePEc:fip:fedhep:y:2004:i:qiv:p:12-31:n:v.28no.4

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Related research

Keywords: Inflation (Finance);

References

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  1. James H. Stock & Mark W. Watson, 2001. "Forecasting output and inflation: the role of asset prices," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
  2. Lawrence J. Christiano & Terry J. Fitzgerald, 1999. "The Band Pass Filter," NBER Working Papers 7257, National Bureau of Economic Research, Inc.
    • Lawrence J. Christiano & Terry J. Fitzgerald, 2003. "The Band Pass Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 435-465, 05.
  3. Todd E. Clark & Michael W. McCracken, 2003. "The predictive content of the output gap for inflation : resolving in-sample and out-of-sample evidence," Research Working Paper RWP 03-06, Federal Reserve Bank of Kansas City.
  4. Stephen G. Cecchetti & Rita S. Chu & Charles Steindel, 2000. "The unreliability of inflation indicators," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 6(Apr).
  5. Andrew Atkeson & Lee E. Ohanian., 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-11.
  6. Christopher A. Sims, 2002. "The Role of Models and Probabilities in the Monetary Policy Process," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 33(2), pages 1-62.
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Citations

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Cited by:
  1. Todd E. Clark & Michael W. McCracken, 2006. "Combining forecasts from nested models," Research Working Paper RWP 06-02, Federal Reserve Bank of Kansas City.
  2. Lanne, Markku & Luoma, Arto & Luoto, Jani, 2008. "A Naïve Sticky Information Model of Households’ Inflation Expectations," MPRA Paper 8663, University Library of Munich, Germany.
  3. Andrew Ang & Geert Bekaert & Min Wei, 2006. "Do macro variables, asset markets, or surveys forecast inflation better?," Finance and Economics Discussion Series 2006-15, Board of Governors of the Federal Reserve System (U.S.).
  4. Doyle, Matthew, 2006. "Empirical Phillips Curves in OECD Countries: Has There Been A Common Breakdown?," Staff General Research Papers 12684, Iowa State University, Department of Economics.
  5. James H. Stock & Mark W. Watson, 2008. "Phillips Curve Inflation Forecasts," NBER Working Papers 14322, National Bureau of Economic Research, Inc.

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