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Modeling Inflation After the Crisis

Author

Listed:
  • James H. Stock

    (Harvard University)

  • Mark W. Watson

    (Princeton University)

Abstract

In the United States, the rate of price inflation falls in recessions. Turning this observation into a useful inflation forecasting equation is difficult because of multiple sources of time variation in the inflation process, including changes in Fed policy and credibility. We propose a tightly parameterized model in which the deviation of inflation from a stochastic trend (which we interpret as long-term expected inflation) reacts stably to a new gap measure, which we call the unemployment recession gap. The short-term response of inflation to an increase in this gap is stable, but the long-term response depends on the resilience, or anchoring, of trend inflation. Dynamic simulations (given the path of unemployment) match the paths of inflation during post-1960 downturns, including the current one.

Suggested Citation

  • James H. Stock & Mark W. Watson, 2010. "Modeling Inflation After the Crisis," Working Papers 2010-1, Princeton University. Economics Department..
  • Handle: RePEc:pri:econom:2010-1
    as

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    File URL: http://www.princeton.edu/~mwatson/papers/stock-watson_frbkc_2010.pdf
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    References listed on IDEAS

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    11. 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.
    12. Mr. Andre Meier, 2010. "Still Minding the Gap—Inflation Dynamics during Episodes of Persistent Large Output Gaps," IMF Working Papers 2010/189, International Monetary Fund.
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    15. 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.
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    Citations

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    Cited by:

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    2. Bańbura, Marta & Bobeica, Elena, 2023. "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 364-390.
    3. Ivașcu Codruț, 2023. "Can Machine Learning Models Predict Inflation?," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 17(1), pages 1748-1756, July.
    4. Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1145-1162.
    5. Zhu, Yinchu & Timmermann, Allan, 2022. "Conditional rotation between forecasting models," Journal of Econometrics, Elsevier, vol. 231(2), pages 329-347.
    6. Aguiar-Conraria, Luís & Martins, Manuel M.F. & Soares, Maria Joana, 2023. "The Phillips curve at 65: Time for time and frequency," Journal of Economic Dynamics and Control, Elsevier, vol. 151(C).
    7. Hany Guirguis & Vaneesha Boney Dutra & Zoe McGreevy, 2022. "The impact of global economies on US inflation: A test of the Phillips curve," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(3), pages 575-592, July.
    8. Tomohide Mineyama, 2023. "Downward Nominal Wage Rigidity and Inflation Dynamics during and after the Great Recession," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(5), pages 1213-1244, August.
    9. Antonello D'Agostino & Caterina Mendicino & Federico Puglisi, 2022. "Expectation‐Driven Cycles and the Changing Dynamics of Unemployment," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(7), pages 2173-2191, October.
    10. Guglielmo Maria Caporale & Luis Alberiko Gil-Alana & Carlos Poza, 2022. "Inflation in the G7 countries: persistence and structural breaks," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(3), pages 493-506, July.
    11. Timmermann, Allan & Zhu, Yinchu, 2021. "Conditional Rotation Between Forecasting Models," CEPR Discussion Papers 15917, C.E.P.R. Discussion Papers.
    12. Enja Erker, 2024. "Forecasting medical inflation in the European Union using the ARIMA model," Public Sector Economics, Institute of Public Finance, vol. 48(1), pages 39-56.

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    More about this item

    Keywords

    Inflation;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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