IDEAS home Printed from https://ideas.repec.org/p/rdg/emxxdp/em-dp2023-05.html
   My bibliography  Save this paper

Do Professional Forecasters' Phillips Curves Incorporate the Beliefs of Others?

Author

Listed:
  • Michael P. Clements

    (ICMA Centre, Henley Business School, University of Reading)

  • Shixuan Wang

    (Department of Economics, University of Reading)

Abstract

We apply functional data analysis to survey expectations data, and show that functional principal component analysis combined with functional regression analysis is a fruitful way of capturing the effects of others’ forecasts on a respondent’s inflation forecasts. We estimate forward-looking Phillips curves on each respondent’s inflation and unemployment rate forecasts, and show that for nearly a half of the respondents the forecasts of others are important. The functional principal components of the cross-sectional distributions of forecasts are shown to capture characteristics other than the mean or consensus forecast, and include forecaster disagreement.

Suggested Citation

  • Michael P. Clements & Shixuan Wang, 2023. "Do Professional Forecasters' Phillips Curves Incorporate the Beliefs of Others?," Economics Discussion Papers em-dp2023-05, Department of Economics, University of Reading.
    • Handle: RePEc:rdg:emxxdp:em-dp2023-05
    as

    Download full text from publisher

    File URL: https://research.reading.ac.uk/economics/wp-content/uploads/sites/87/2023/02/emdp202305.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Adams, Patrick A. & Adrian, Tobias & Boyarchenko, Nina & Giannone, Domenico, 2021. "Forecasting macroeconomic risks," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1173-1191.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Noori, Mohammad & Hitaj, Asmerilda, 2023. "Dissecting hedge funds' strategies," International Review of Financial Analysis, Elsevier, vol. 85(C).
    2. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    3. Valentina Aprigliano & Alessandro Borin & Francesco Paolo Conteduca & Simone Emiliozzi & Marco Flaccadoro & Sabina Marchetti & Stefania Villa, 2021. "Forecasting Italian GDP growth with epidemiological data," Questioni di Economia e Finanza (Occasional Papers) 664, Bank of Italy, Economic Research and International Relations Area.
    4. Iacopini, Matteo & Poon, Aubrey & Rossini, Luca & Zhu, Dan, 2023. "Bayesian mixed-frequency quantile vector autoregression: Eliciting tail risks of monthly US GDP," Journal of Economic Dynamics and Control, Elsevier, vol. 157(C).
    5. Jan Prüser & Florian Huber, 2024. "Nonlinearities in macroeconomic tail risk through the lens of big data quantile regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 269-291, March.
    6. Efrem Castelnuovo & Lorenzo Mori, 2022. "Uncertainty, Skewness and the Business Cycle - Through the MIDAS Lens," CAMA Working Papers 2022-69, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Daniel Gros, 2021. "High Public Debt in an Uncertain World: Post-Covid-19 Dangers for Public Finance," EconPol Policy Brief 38, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    8. Iseringhausen, Martin, 2024. "A time-varying skewness model for Growth-at-Risk," International Journal of Forecasting, Elsevier, vol. 40(1), pages 229-246.
    9. Stolbov, Mikhail & Shchepeleva, Maria, 2022. "Modeling global real economic activity: Evidence from variable selection across quantiles," The Journal of Economic Asymmetries, Elsevier, vol. 25(C).
    10. Manfred M. Fischer & Niko Hauzenberger & Florian Huber & Michael Pfarrhofer, 2023. "General Bayesian time‐varying parameter vector autoregressions for modeling government bond yields," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 69-87, January.
    11. Ferrara, Laurent & Mogliani, Matteo & Sahuc, Jean-Guillaume, 2022. "High-frequency monitoring of growth at risk," International Journal of Forecasting, Elsevier, vol. 38(2), pages 582-595.
    12. Manfred M. Fischer & Niko Hauzenberger & Florian Huber & Michael Pfarrhofer, 2021. "General Bayesian time-varying parameter VARs for predicting government bond yields," Papers 2102.13393, arXiv.org.
    13. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    14. Nyholm, Juho & Voutilainen, Ville, 2021. "Quantiles of growth: Household debt and growth vulnerabilities in Finland," BoF Economics Review 2/2021, Bank of Finland.
    15. David Kohns & Tibor Szendrei, 2021. "Decoupling Shrinkage and Selection for the Bayesian Quantile Regression," Papers 2107.08498, arXiv.org.
    16. Jane M. Ryngaert, 2023. "Balance of Risks and the Anchoring of Consumer Expectations," JRFM, MDPI, vol. 16(2), pages 1-18, January.
    17. Nina Boyarchenko & Giovanni Favara & Moritz Schularick, 2022. "Financial Stability Considerations for Monetary Policy: Empirical Evidence and Challenges," Staff Reports 1003, Federal Reserve Bank of New York.
    18. Marian Vavra, 2023. "Bias-Correction in Time Series Quantile Regression Models," Working and Discussion Papers WP 3/2023, Research Department, National Bank of Slovakia.
    19. Sharpe, Steven A. & Sinha, Nitish R. & Hollrah, Christopher A., 2023. "The power of narrative sentiment in economic forecasts," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1097-1121.
    20. Sokol, Andrej, 2021. "Fan charts 2.0: flexible forecast distributions with expert judgement," Working Paper Series 2624, European Central Bank.

    More about this item

    Keywords

    inflation forecasting; functional data analysis; Survey of Professional Forecasters; forecast disagreement;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rdg:emxxdp:em-dp2023-05. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Alexander Mihailov (email available below). General contact details of provider: https://edirc.repec.org/data/derdguk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.