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What Does Forecaster Disagreement Tell Us about the State of the Economy?

Author

Listed:
  • Constantin Bürgi

    (St. Mary’s College of Maryland)

  • Tara M. Sinclair

    (The George Washington University)

Abstract

This paper 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, 2020. "What Does Forecaster Disagreement Tell Us about the State of the Economy?," Working Papers 2020-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    • Handle: RePEc:gwc:wpaper:2020-001
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    References listed on IDEAS

    as
    1. Sinclair, Tara M. & Joutz, Fred & Stekler, H.O., 2010. "Can the Fed predict the state of the economy?," Economics Letters, Elsevier, vol. 108(1), pages 28-32, July.
    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. Proaño, Christian R. & Theobald, Thomas, 2014. "Predicting recessions with a composite real-time dynamic probit model," International Journal of Forecasting, Elsevier, vol. 30(4), pages 898-917.
    4. Constantin Burgi, 2016. "What Do We Lose When We Average Expectations?," Working Papers 2016-013, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    5. Fatima Mboup & Ardy Wurtzel, 2018. "Battle of the Forecasts: Mean vs. Median as the Survey of Professional Forecasters’ Consensus," Research Brief, Federal Reserve Bank of Philadelphia, issue Q4.
    6. Victor Zarnowitz, 1986. "The Record and Improvability of Economic Forecasting," NBER Working Papers 2099, National Bureau of Economic Research, Inc.
    7. Bomberger, William A, 1996. "Disagreement as a Measure of Uncertainty," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 28(3), pages 381-392, August.
    8. Dopke, Jorg & Fritsche, Ulrich, 2006. "When do forecasters disagree? An assessment of German growth and inflation forecast dispersion," International Journal of Forecasting, Elsevier, vol. 22(1), pages 125-135.
    9. Scott R. Baker & Tucker S. McElroy & Xuguang S. Sheng, 2020. "Expectation Formation Following Large, Unexpected Shocks," The Review of Economics and Statistics, MIT Press, vol. 102(2), pages 287-303, May.
    10. Kajal Lahiri & Xuguang Sheng, 2010. "Measuring forecast uncertainty by disagreement: The missing link," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 514-538.
    11. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    12. Patton, Andrew J. & Timmermann, Allan, 2010. "Why do forecasters disagree? Lessons from the term structure of cross-sectional dispersion," Journal of Monetary Economics, Elsevier, vol. 57(7), pages 803-820, October.
    13. Constantin Bürgi & Tara M. Sinclair, 2017. "A nonparametric approach to identifying a subset of forecasters that outperforms the simple average," Empirical Economics, Springer, vol. 53(1), pages 101-115, August.
    14. Fintzen, David & Stekler, H. O., 1999. "Why did forecasters fail to predict the 1990 recession?," International Journal of Forecasting, Elsevier, vol. 15(3), pages 309-323, July.
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    16. Ozturk, Ezgi O. & Sheng, Xuguang Simon, 2018. "Measuring global and country-specific uncertainty," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 276-295.
    17. Driver, Ciaran & Trapani, Lorenzo & Urga, Giovanni, 2013. "On the use of cross-sectional measures of forecast uncertainty," International Journal of Forecasting, Elsevier, vol. 29(3), pages 367-377.
    18. Travis J. Berge & Òscar Jordà, 2011. "Evaluating the Classification of Economic Activity into Recessions and Expansions," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(2), pages 246-277, April.
    19. Batchelor, Roy & Dua, Pami, 1993. "Survey vs ARCH Measures of Inflation Uncertainty," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 55(3), pages 341-353, August.
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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, 2024. "Firm Level Expectations and Macroeconomic Conditions: Underpinnings and Disagreement," CAMA Working Papers 2024-05, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    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 presid," Review of Development Economics, Wiley Blackwell, vol. 26(4), pages 2356-2393, November.

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    More about this item

    Keywords

    Expectations; SPF; Uncertainty; Dynamic Probit;
    All these keywords.

    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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