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Measuring Inflation Expectations Uncertainty Using High-Frequency Data

Author

Listed:
  • Joshua C C Chan
  • Yong Song

Abstract

Inflation expectations play a key role in determining future economic outcomes. The associated uncertainty provides a direct gauge of how well-anchored the inflation expectations are. We construct a model-based measure of inflation expectations uncertainty by augmenting a standard unobserved components model of inflation with information from noisy and possibly biased measures of inflation expectations obtained from financial markets. This new model-based measure of inflation expectations uncertainty is more accurately estimated and can provide valuable information for policymakers. Using US data, we find significant changes in inflation expectations uncertainty during the Great Recession.

Suggested Citation

  • Joshua C C Chan & Yong Song, 2017. "Measuring Inflation Expectations Uncertainty Using High-Frequency Data," CAMA Working Papers 2017-61, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    • Handle: RePEc:een:camaaa:2017-61
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    File URL: https://crawford.anu.edu.au/sites/default/files/2025-01/61_2017_chan_song.pdf
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    Cited by:

    1. Marta Bañbura & Danilo Leiva-León & Jan-Oliver Menz, 2021. "Do inflation expectations improve model-based inflation Forecasts?," Working Papers 2138, Banco de España.
    2. Miescu, Mirela S., 2023. "Uncertainty shocks in emerging economies: A global to local approach for identification," European Economic Review, Elsevier, vol. 154(C).
    3. Carlomagno, Guillermo & Fornero, Jorge & Sansone, Andrés, 2023. "A proposal for constructing and evaluating core inflation measures," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(3).
    4. Ren, Yi-Shuai & Klein, Tony & Jiang, Yong & Ma, Chao-Qun & Yang, Xiao-Guang, 2024. "Dynamic spillovers among global oil shocks, economic policy uncertainty, and inflation expectation uncertainty under extreme shocks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    5. Chen, Ji & Yang, Xinglin & Liu, Xiliang, 2022. "Learning, disagreement and inflation forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    6. Fuest, Angela & Schmidt, Torsten, 2020. "Inflation expectation uncertainty in a New Keynesian framework," Ruhr Economic Papers 867, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    7. Ryngaert, Jane M., 2022. "Inflation disasters and consumption," Journal of Monetary Economics, Elsevier, vol. 129(S), pages 67-81.
    8. Carola Binder & Wesley Janson & Randal J. Verbrugge, 2019. "Thinking Outside the Box: Do SPF Respondents Have Anchored Inflation Expectations?," Working Papers 19-15, Federal Reserve Bank of Cleveland.

    More about this item

    Keywords

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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