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In search of a robust inflation forecast

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  • 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.

Suggested Citation

  • Scott Brave & Jonas D. M. Fisher, 2004. "In search of a robust inflation forecast," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 28(Q IV), pages 12-31.
  • Handle: RePEc:fip:fedhep:y:2004:i:qiv:p:12-31:n:v.28no.4
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    References listed on IDEAS

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    1. 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, May.
    2. Clark, Todd E. & McCracken, Michael W., 2006. "The Predictive Content of the Output Gap for Inflation: Resolving In-Sample and Out-of-Sample Evidence," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1127-1148, August.
    3. 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.
    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. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    6. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 25(Win), pages 2-11.
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    Citations

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    Cited by:

    1. Scott Brave & Charles Gascon & William Kluender & Thomas Walstrum, 2019. "Predicting Benchmarked US State Employment Data in Realtime," Working Paper Series WP 2019-11, Federal Reserve Bank of Chicago.
    2. Lanne, Markku & Luoma, Arto & Luoto, Jani, 2009. "A naïve sticky information model of households' inflation expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 33(6), pages 1332-1344, June.
    3. Chletsos, Michael & Drosou, Vasiliki & Roupakias, Stelios, 2016. "Can Phillips curve explain the recent behavior of inflation? Further evidence from USA and Canada," The Journal of Economic Asymmetries, Elsevier, vol. 14(PA), pages 20-28.
    4. Todd E. Clark & Michael W. McCracken, 2009. "Combining Forecasts from Nested Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 303-329, June.
    5. 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.
    6. James H. Stock & Mark W. Watson, 2008. "Phillips curve inflation forecasts," Conference Series ; [Proceedings], Federal Reserve Bank of Boston.
    7. Novikova Natalia & Volkov Dmitry, 2012. "Modelling core inflation in Ukraine in 2003-2012," EERC Working Paper Series 12/12e, EERC Research Network, Russia and CIS.
    8. Christian Gillitzer & Jonathan Kearns, 2007. "Forecasting with Factors: The Accuracy of Timeliness," RBA Research Discussion Papers rdp2007-03, Reserve Bank of Australia.
    9. Doyle, Matthew, 2006. "Empirical Phillips Curves in OECD Countries: Has There Been A Common Breakdown?," Staff General Research Papers Archive 12684, Iowa State University, Department of Economics.

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    Keywords

    Inflation (Finance);

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