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Rounding behaviour of professional macro-forecasters

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  • Clements, Michael P.

Abstract

The rounding of point forecasts of CPI inflation and the unemployment rate by U.S. Professional Forecasters is modest. There is little evidence that forecasts are rounded to a greater extent in response to higher perceived uncertainty surrounding future outcomes. There is clear evidence that the probability of decline forecasts are rounded: over half of the forecast probabilities of decline in the current quarter are multiples of ten. It is found here that the rounding of these probabilities correlates with worse accuracy, although it is also of note here that worse (less accurate) forecasters might round more as opposed to the degree of rounding per se worsening accuracy. By simulating the loss from rounding for a set of efficient forecasters, it is demonstrated that the explanation that respondents round otherwise efficient forecasts is implausible, and that the contribution of rounding is of minor importance.

Suggested Citation

  • Clements, Michael P., 2021. "Rounding behaviour of professional macro-forecasters," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1614-1631.
  • Handle: RePEc:eee:intfor:v:37:y:2021:i:4:p:1614-1631
    DOI: 10.1016/j.ijforecast.2021.03.003
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    1. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    2. Gergely Ganics & Barbara Rossi & Tatevik Sekhposyan, 2019. "From fixed-event to fixed-horizon density forecasts: obtaining measures of multi-horizon uncertainty from survey density forecasts," Working Papers 1947, Banco de España.
    3. Pedro Bordalo & Nicola Gennaioli & Yueran Ma & Andrei Shleifer, 2020. "Overreaction in Macroeconomic Expectations," American Economic Review, American Economic Association, vol. 110(9), pages 2748-2782, September.
    4. Engelberg, Joseph & Manski, Charles F. & Williams, Jared, 2009. "Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 30-41.
    5. 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.
    6. Michael P. Clements, 2020. "Are Some Forecasters’ Probability Assessments of Macro Variables Better Than Those of Others?," Econometrics, MDPI, vol. 8(2), pages 1-16, May.
    7. Michael P. Clements, 2014. "Forecast Uncertainty- Ex Ante and Ex Post : U.S. Inflation and Output Growth," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 206-216, April.
    8. Rossi, Barbara & Ganics, Gergely & Sekhposyan, Tatevik, 2020. "From Fixed-event to Fixed-horizon Density Forecasts: Obtaining Measures of Multi-horizon Uncertainty from Survey Density Foreca," CEPR Discussion Papers 14267, C.E.P.R. Discussion Papers.
    9. Geoff Kenny & Thomas Kostka & Federico Masera, 2014. "How Informative are the Subjective Density Forecasts of Macroeconomists?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(3), pages 163-185, April.
    10. Jacob A. Mincer, 1969. "Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance," NBER Books, National Bureau of Economic Research, Inc, number minc69-1, March.
    11. Michael P. Clements, 2011. "An Empirical Investigation of the Effects of Rounding on the SPF Probabilities of Decline and Output Growth Histograms," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(1), pages 207-220, February.
    12. Dean Croushore, 1993. "Introducing: the survey of professional forecasters," Business Review, Federal Reserve Bank of Philadelphia, issue Nov, pages 3-15.
    13. Lahiri, Kajal & Wang, J. George, 2013. "Evaluating probability forecasts for GDP declines using alternative methodologies," International Journal of Forecasting, Elsevier, vol. 29(1), pages 175-190.
    14. Binder, Carola C., 2017. "Measuring uncertainty based on rounding: New method and application to inflation expectations," Journal of Monetary Economics, Elsevier, vol. 90(C), pages 1-12.
    15. Michael P. Clements, 2015. "Are Professional Macroeconomic Forecasters Able To Do Better Than Forecasting Trends?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(2-3), pages 349-382, March.
    16. Manski, Charles F. & Molinari, Francesca, 2010. "Rounding Probabilistic Expectations in Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 219-231.
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    Cited by:

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    2. Cornand, Camille & Hubert, Paul, 2022. "Information frictions across various types of inflation expectations," European Economic Review, Elsevier, vol. 146(C).
    3. Camille Cornand & Paul Hubert, 2021. "Information frictions in inflation expectations among five types of economic agents," SciencePo Working papers Main halshs-03351632, HAL.
    4. Camille Cornand & Paul Hubert, 2021. "Information frictions in inflation expectations among five types of economic agents," Working Papers hal-03468918, HAL.
    5. Zhao, Yongchen, 2023. "Internal consistency of household inflation expectations: Point forecasts vs. density forecasts," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1713-1735.

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