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Testing for Forecast Consensus

Author

Listed:
  • Gregory, Allan W
  • Smith, Gregor W
  • Yetman, James

Abstract

A panel of forecasts may be defined to be in consensus when individual forecasters place identical weights on a common latent variable. We suggest this definition and formulate a dynamic latent-variable model to test for consensus. This method also tests whether it is valid to use the mean forecast as the consensus forecast. In applications to surveys of U.S. macroeconomic forecasters, there is greater consensus in forecasts for output growth than for inflation or unemployment, but idiosyncratic forecast autocorrelation from year to year is present for most forecasters.

Suggested Citation

  • Gregory, Allan W & Smith, Gregor W & Yetman, James, 2001. "Testing for Forecast Consensus," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 34-43, January.
    • Handle: RePEc:bes:jnlbes:v:19:y:2001:i:1:p:34-43
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    Citations

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    Cited by:

    1. Koo, Kyung Ah & Kong, Woo-Seok & Park, Seon Uk & Lee, Joon Ho & Kim, Jaeuk & Jung, Huicheul, 2017. "Sensitivity of Korean fir (Abies koreana Wils.), a threatened climate relict species, to increasing temperature at an island subalpine area," Ecological Modelling, Elsevier, vol. 353(C), pages 5-16.
    2. Kapetanios, George & Mitchell, James & Shin, Yongcheol, 2014. "A nonlinear panel data model of cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 179(2), pages 134-157.
    3. George Kapetanios & James Mitchell & Yongcheol Shin, 2010. "A Nonlinear Panel Model of Cross-sectional Dependence," Working Papers 673, Queen Mary University of London, School of Economics and Finance.
    4. 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.
    5. Lahiri, Kajal & Sheng, Xuguang, 2010. "Learning and heterogeneity in GDP and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 26(2), pages 265-292, April.
    6. Song, ChiUng & Boulier, Bryan L. & Stekler, Herman O., 2009. "Measuring consensus in binary forecasts: NFL game predictions," International Journal of Forecasting, Elsevier, vol. 25(1), pages 182-191.
    7. Jeong Soo Park & Donghui Choi & Youngha Kim, 2020. "Potential Distribution of Goldenrod ( Solidago altissima L.) during Climate Change in South Korea," Sustainability, MDPI, vol. 12(17), pages 1-11, August.
    8. Bizer, Kilian & Meub, Lukas & Proeger, Till & Spiwoks, Markus, 2014. "Strategic coordination in forecasting: An experimental study," University of Göttingen Working Papers in Economics 195, University of Goettingen, Department of Economics.
    9. Emilian Dobrescu, 2014. "A Hybrid Forecasting Approach," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 16(35), pages 390-390, February.
    10. repec:wrk:wrkemf:03 is not listed on IDEAS
    11. Lahiri, Kajal & Sheng, Xuguang, 2008. "Evolution of forecast disagreement in a Bayesian learning model," Journal of Econometrics, Elsevier, vol. 144(2), pages 325-340, June.
    12. Paxton, Julia & Thraen, Cameron, 2003. "An application of Mean-Covariance Structure Models for the analysis of group lending behavior," Journal of Policy Modeling, Elsevier, vol. 25(9), pages 863-868, December.
    13. Gregory, Allan W. & Yetman, James, 2004. "The evolution of consensus in macroeconomic forecasting," International Journal of Forecasting, Elsevier, vol. 20(3), pages 461-473.

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