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What does forecaster disagreement tell us about the state of the economy?

Author

Listed:
  • Constantin Bürgi
  • Tara M. Sinclair

Abstract

This article shows in a simple model that the part of uncertainty measured by forecaster disagreement rises in advance of and during recessions. Subsequently, it is tested using the Survey of Professional Forecasters in a dynamic probit model. It is shown that increases in disagreement help predict recessions in an out-of-sample context for the US.

Suggested Citation

  • Constantin Bürgi & Tara M. Sinclair, 2021. "What does forecaster disagreement tell us about the state of the economy?," Applied Economics Letters, Taylor & Francis Journals, vol. 28(1), pages 49-53, January.
    • Handle: RePEc:taf:apeclt:v:28:y:2021:i:1:p:49-53
    DOI: 10.1080/13504851.2020.1730751
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    Cited by:

    1. Martinez, Andrew & Schibuola, Alex, 2021. "The Expectations Gap: An Alternative Measure of Economic Slack," Working Papers 11284, George Mason University, Mercatus Center.
    2. Monique Reid & Pierre Siklos, 2025. "Firm‐Level Expectations and Macroeconomic Conditions: Underpinnings and Disagreement," South African Journal of Economics, Economic Society of South Africa, vol. 93(2), pages 203-218, June.
    3. Glas, Alexander & Heinisch, Katja, 2021. "Conditional macroeconomic forecasts: Disagreement, revisions and forecast errors," IWH Discussion Papers 7/2021, Halle Institute for Economic Research (IWH).
    4. Gabriel Caldas Montes & Paulo Henrique Lourenço Luna, 2022. "Do fiscal opacity, fiscal impulse, and fiscal credibility affect disagreement about economic growth forecasts? Empirical evidence from Brazil considering the period of political instability and presidential impeachment," Review of Development Economics, Wiley Blackwell, vol. 26(4), pages 2356-2393, November.
    5. Andrew B. Martinez & Alexander D. Schibuola & David Beckworth, 2026. "The Reliability of the Nominal GDP Expectations Gap," International Journal of Central Banking, International Journal of Central Banking, vol. 22(2), pages 525-557, April.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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