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Testing for Forecast Consensus

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Author Info

  • 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.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 19 (2001)
Issue (Month): 1 (January)
Pages: 34-43

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Handle: RePEc:bes:jnlbes:v:19:y:2001:i:1:p:34-43

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Cited by:
  1. Gregory, Allan W. & Yetman, James, 2004. "The evolution of consensus in macroeconomic forecasting," International Journal of Forecasting, Elsevier, vol. 20(3), pages 461-473.
  2. James Mitchell & George Kapetanios & Yongcheol Shin, 2012. "A Nonlinear Panel Data Model of Cross-Sectional Dependence," Discussion Papers in Economics 12/01, Department of Economics, University of Leicester.
  3. 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.
  4. 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.
  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. ChiUng Song & Bryan L. Boulier & Herman O. Stekler, 2008. "Measuring Consensus in Binary Forecasts: NFL Game Predictions," Working Papers 2008-006, The George Washington University, Department of Economics, Research Program on Forecasting.
  7. 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.
  8. Emilian Dobrescu, 2014. "A Hybrid Forecasting Approach," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 16(35), pages 390, February.
  9. Bizer, Kilian & Meub, Lukas & Proeger, Till & Spiwoks, Markus, 2014. "Strategic coordination in forecasting: An experimental study," Center for European, Governance and Economic Development Research Discussion Papers 195, University of Goettingen, Department of Economics.
  10. 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.

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