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Finding a Role for Slack in Real-Time Inflation Forecasting

Author

Listed:
  • N. Kundan Kishor

    (University of Wisconsin-Milwaukee)

  • Evan F. Koenig

    (Federal Reserve Bank of Dallas)

Abstract

Real-time forecasting of PCE inflation is most successful when headline inflation is stripped of high-frequency noise and core inflation's trend and cycle are separately forecasted. It proves helpful, additionally, to allow cyclical inflation to respond to labor market slack, to allow for a late-1990s break in the behavior of trend inflation, and to explicitly model revisions to headline inflation. We do all of this within the context of an unobserved-common-components model of inflation and slack. The model's real-time inflation forecasts are significantly more accurate than those generated by benchmark models. That outperformance and the finding that cyclical inflation responds to slack are robust to an alternative measure of slack, an alternative model of trend inflation, and an alternative treatment of data revisions.

Suggested Citation

  • N. Kundan Kishor & Evan F. Koenig, 2022. "Finding a Role for Slack in Real-Time Inflation Forecasting," International Journal of Central Banking, International Journal of Central Banking, vol. 18(2), pages 245-282, June.
  • Handle: RePEc:ijc:ijcjou:y:2022:q:2:a:6
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    References listed on IDEAS

    as
    1. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    2. Basistha, Arabinda & Nelson, Charles R., 2007. "New measures of the output gap based on the forward-looking new Keynesian Phillips curve," Journal of Monetary Economics, Elsevier, vol. 54(2), pages 498-511, March.
    3. Clark, Peter K., 1989. "Trend reversion in real output and unemployment," Journal of Econometrics, Elsevier, vol. 40(1), pages 15-32, January.
    4. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    5. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 468-479.
    6. repec:taf:jnlbes:v:30:y:2012:i:2:p:181-190 is not listed on IDEAS
    7. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    8. 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.
    9. James C. Morley & Charles R. Nelson & Eric Zivot, 2003. "Why Are the Beveridge-Nelson and Unobserved-Components Decompositions of GDP So Different?," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 235-243, May.
    10. Rossi, Barbara & Sekhposyan, Tatevik, 2010. "Have economic models' forecasting performance for US output growth and inflation changed over time, and when?," International Journal of Forecasting, Elsevier, vol. 26(4), pages 808-835, October.
    11. A. W. Phillips, 1958. "The Relation Between Unemployment and the Rate of Change of Money Wage Rates in the United Kingdom, 1861–1957," Economica, London School of Economics and Political Science, vol. 25(100), pages 283-299, November.
    12. Arabinda Basistha & Richard Startz, 2008. "Measuring the NAIRU with Reduced Uncertainty: A Multiple-Indicator Common-Cycle Approach," The Review of Economics and Statistics, MIT Press, vol. 90(4), pages 805-811, November.
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    More about this item

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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