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Forecast disagreement in the Survey of Professional Forecasters

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  • Keith Sill

Abstract

To enact effective policies and spend resources efficiently, firms, policymakers, and markets need accurate economic forecasts. But even though economists generally work with similar models and data, their projections often range widely. To better understand why, Keith Sill explores what the evidence and theories say about how forecasters form their views.

Suggested Citation

  • Keith Sill, 2014. "Forecast disagreement in the Survey of Professional Forecasters," Business Review, Federal Reserve Bank of Philadelphia, issue Q2, pages 15-24.
    • Handle: RePEc:fip:fedpbr:00010
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    File URL: https://www.philadelphiafed.org/-/media/frbp/assets/economy/articles/business-review/2014/q2/brQ214_forecast_disagreement.pdf
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    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, President and Fellows of Harvard College, vol. 117(4), pages 1295-1328.
    3. Clements, Michael P., 2012. "Do professional forecasters pay attention to data releases?," International Journal of Forecasting, Elsevier, vol. 28(2), pages 297-308.
    4. Lucas, Robert Jr., 1972. "Expectations and the neutrality of money," Journal of Economic Theory, Elsevier, vol. 4(2), pages 103-124, April.
    5. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    6. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
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    Cited by:

    1. Vania Esady, 2019. "Real and Nominal Effects of Monetary Shocks under Time-Varying Disagreement," CESifo Working Paper Series 7956, CESifo.
    2. Gillmann, Niels & Kim, Alisa, 2021. "Quantification of Economic Uncertainty: a deep learning approach," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242421, Verein für Socialpolitik / German Economic Association.
    3. Esady, Vania, 2022. "Real and nominal effects of monetary shocks under time-varying disagreement," Bank of England working papers 1007, Bank of England.

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