IDEAS home Printed from https://ideas.repec.org/p/fip/fedcwq/90000.html
   My bibliography  Save this paper

All Forecasters Are Not the Same: Time-Varying Predictive Ability across Forecast Environments

Author

Listed:
  • Robert W. Rich
  • Joseph Tracy

Abstract

This paper examines data from the European Central Bank’s Survey of Professional Forecasters to investigate whether participants display equal predictive performance. We use panel data models to evaluate point- and density-based forecasts of real GDP growth, inflation, and unemployment. The results document systematic differences in participants’ forecast accuracy that are not time invariant, but instead vary with the difficulty of the forecasting environment. Specifically, we find that some participants display higher relative accuracy in tranquil environments, while others display higher relative accuracy in volatile environments. We also find that predictive performance is positively correlated across target variables and horizons, with density forecasts generating stronger correlation patterns. Taken together, the results support the development of expectations models featuring persistent heterogeneity.

Suggested Citation

  • Robert W. Rich & Joseph Tracy, 2021. "All Forecasters Are Not the Same: Time-Varying Predictive Ability across Forecast Environments," Working Papers 202106, Federal Reserve Bank of Cleveland.
  • Handle: RePEc:fip:fedcwq:90000
    DOI: 10.26509/frbc-wp-202106
    as

    Download full text from publisher

    File URL: https://doi.org/10.26509/frbc-wp-202106
    File Function: Full Text
    Download Restriction: no

    File URL: https://libkey.io/10.26509/frbc-wp-202106?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Olivier Coibion & Yuriy Gorodnichenko, 2012. "What Can Survey Forecasts Tell Us about Information Rigidities?," Journal of Political Economy, University of Chicago Press, vol. 120(1), pages 116-159.
    2. N. Gregory Mankiw & Ricardo Reis, 2002. "Sticky Information versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve," The Quarterly Journal of Economics, Oxford University Press, vol. 117(4), pages 1295-1328.
    3. Antonello D’Agostino & Kieran Mcquinn & Karl Whelan, 2012. "Are Some Forecasters Really Better Than Others?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(4), pages 715-732, June.
    4. Kenny, Geoff & Genre, Véronique & Bowles, Carlos & Friz, Roberta & Meyler, Aidan & Rautanen, Tuomas, 2007. "The ECB survey of professional forecasters (SPF) - A review after eight years' experience," Occasional Paper Series 59, European Central Bank.
    5. Meyler, Aidan, 2020. "Forecast performance in the ECB SPF: ability or chance?," Working Paper Series 2371, European Central Bank.
    6. Batchelor, R A, 1990. "All Forecasters Are Equal," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 143-144, January.
    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. Robert Rich & Joseph Tracy, 2021. "A Closer Look at the Behavior of Uncertainty and Disagreement: Micro Evidence from the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(1), pages 233-253, February.
    2. Michael Clements, 2016. "Are Macro-Forecasters Essentially The Same? An Analysis of Disagreement, Accuracy and Efficiency," ICMA Centre Discussion Papers in Finance icma-dp2016-08, Henley Business School, University of Reading.
    3. George-Marios Angeletos & Chen Lian, 2018. "Forward Guidance without Common Knowledge," American Economic Review, American Economic Association, vol. 108(9), pages 2477-2512, September.
    4. Olivier Coibion & Yuriy Gorodnichenko & Saten Kumar, 2018. "How Do Firms Form Their Expectations? New Survey Evidence," American Economic Review, American Economic Association, vol. 108(9), pages 2671-2713, September.
    5. Michael P. Clements, 2014. "US Inflation Expectations and Heterogeneous Loss Functions, 1968–2010," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 1-14, January.
    6. Candian, Giacomo, 2019. "Information frictions and real exchange rate dynamics," Journal of International Economics, Elsevier, vol. 116(C), pages 189-205.
    7. Martin Geiger & Johann Scharler, 2021. "How Do People Interpret Macroeconomic Shocks? Evidence from U.S. Survey Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(4), pages 813-843, June.
    8. Paul Hubert, 2014. "FOMC Forecasts as a Focal Point for Private Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(7), pages 1381-1420, October.
    9. Byeongdeuk Jang & Young Se Kim, 2017. "Driving Forces of Inflation Expectations," Korean Economic Review, Korean Economic Association, vol. 33, pages 207-237.
    10. Shioji, Etsuro, 2015. "Time varying pass-through: Will the yen depreciation help Japan hit the inflation target?," Journal of the Japanese and International Economies, Elsevier, vol. 37(C), pages 43-58.
    11. Mackowiak, Bartosz Adam & Matejka, Filip & Wiederholt, Mirko, 2020. "Rational Inattention: A Review," CEPR Discussion Papers 15408, C.E.P.R. Discussion Papers.
    12. Keith Sill, 2014. "Forecast disagreement in the Survey of Professional Forecasters," Business Review, Federal Reserve Bank of Philadelphia, issue Q2, pages 15-24.
    13. Oleksiy Kryvtsov, 2005. "Information Flows and Aggregate Persistence," Computing in Economics and Finance 2005 416, Society for Computational Economics.
    14. Larsen, Vegard H. & Thorsrud, Leif Anders & Zhulanova, Julia, 2021. "News-driven inflation expectations and information rigidities," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 507-520.
    15. Serafín Frache & Rodrigo Lluberas, 2017. "New information and inflation expectations among firms," Documentos de trabajo 2017013, Banco Central del Uruguay.
    16. Paul Hubert, 2015. "The Influence and Policy Signalling Role of FOMC Forecasts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(5), pages 655-680, October.
    17. Christopher S. Sutherland, 2020. "Forward Guidance and Expectation Formation: A Narrative Approach," Staff Working Papers 20-40, Bank of Canada.
    18. Benedetto Molinari, 2014. "Sticky information and inflation persistence: evidence from the U.S. data," Empirical Economics, Springer, vol. 46(3), pages 903-935, May.
    19. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.
    20. Cole, Stephen J. & Milani, Fabio, 2021. "Heterogeneity in individual expectations, sentiment, and constant-gain learning," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 627-650.

    More about this item

    Keywords

    professional forecasters; survey data; forecast accuracy; point forecasts; density forecasts; persistent heterogeneity;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:fip:fedcwq:90000. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/frbclus.html .

    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: (email available below). General contact details of provider: https://edirc.repec.org/data/frbclus.html .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.