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Do survey joiners and leavers differ from regular participants? The US SPF GDP growth and inflation forecasts

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  • Clements, Michael P.

Abstract

If ‘learning by doing’ is important for macro-forecasting, newcomers might be different from regular, established participants. Stayers may also differ from the soon-to-leave. We test these conjectures for macro-forecasters’ point predictions of output growth and inflation, and for their histogram forecasts. Histogram forecasts of inflation by both joiners and leavers are found to be less accurate, especially if we suppose that joiners take time to learn. For GDP growth, there is no evidence of differences between the groups in terms of histogram forecast accuracy, although GDP point forecasts by leavers are less accurate. These findings are predicated on forecasters being homogeneous within groups. Allowing for individual fixed effects suggests fewer differences, including leavers’ inflation histogram forecasts being no less accurate.

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  • Clements, Michael P., 2021. "Do survey joiners and leavers differ from regular participants? The US SPF GDP growth and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 37(2), pages 634-646.
  • Handle: RePEc:eee:intfor:v:37:y:2021:i:2:p:634-646
    DOI: 10.1016/j.ijforecast.2020.08.003
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    2. Klein, Tony, 2021. "Agree to Disagree? Predictions of U.S. Nonfarm Payroll Changes between 2008 and 2020 and the Impact of the COVID19 Labor Shock," QBS Working Paper Series 2021/07, Queen's University Belfast, Queen's Business School.
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    4. 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.
    5. Klein, Tony, 2022. "Agree to disagree? Predictions of U.S. nonfarm payroll changes between 2008 and 2020 and the impact of the COVID19 labor shock," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 264-286.

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    More about this item

    Keywords

    Forecast accuracy; Experience; Learning by doing; Probability forecasts; Growth forecasts; Inflation forecasts;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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