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A Multivariate Analysis of Forecast Disagreement: Confronting Models of Disagreement with SPF Data

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  • Dovern, Jonas

Abstract

This paper documents multivariate forecast disagreement among professional forecasters of the Euro area economy and discusses implications for models of heterogeneous expectation formation. Disagreement varies over time and is strongly counter-cyclical. Disagreement is positively correlated with general (economic) uncertainty. Aggregate supply shocks drive disagreement about the long-run state of the economy while aggregate demand shocks have an impact on the level of disagreement about the short-run outlook for the economy. Forecasters disagree about the structure of the economy and the degree to which individual forecasters disagree with the average forecast tends to persist over time. This suggests that models of heterogeneous expectation formation, which are currently not able to generate those last two features, need to be modified. Introducing learning mechanisms and heterogeneous signal-to-noise ratios could reconcile the benchmark model for disagreement with the observed facts.

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  • Dovern, Jonas, 2014. "A Multivariate Analysis of Forecast Disagreement: Confronting Models of Disagreement with SPF Data," Working Papers 0571, University of Heidelberg, Department of Economics.
  • Handle: RePEc:awi:wpaper:0571 Note: This paper is part of http://archiv.ub.uni-heidelberg.de/volltextserver/view/schriftenreihen/sr-3.html
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    References listed on IDEAS

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    1. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    2. Andrew Mountford & Harald Uhlig, 2009. "What are the effects of fiscal policy shocks?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(6), pages 960-992.
    3. R?diger Bachmann & Steffen Elstner & Eric R. Sims, 2013. "Uncertainty and Economic Activity: Evidence from Business Survey Data," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 217-249, April.
    4. 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, Oxford University Press, vol. 117(4), pages 1295-1328.
    5. Olivier Coibion & Yuriy Gorodnichenko, 2015. "Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts," American Economic Review, American Economic Association, pages 2644-2678.
    6. Batchelor, Roy, 2007. "Bias in macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 23(2), pages 189-203.
    7. Dovern, Jonas & Fritsche, Ulrich & Loungani, Prakash & Tamirisa, Natalia, 2015. "Information rigidities: Comparing average and individual forecasts for a large international panel," International Journal of Forecasting, Elsevier, vol. 31(1), pages 144-154.
    8. 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.
    9. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    10. Roy Batchelor, 2007. "Forecaster Behaviour and Bias in Macroeconomic Forecasts," ifo Working Paper Series 39, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    11. 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.
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    Cited by:

    1. Michael Clements, 2016. "Are Macro-Forecasters Essentially The Same? An Analysis of Disagreement, Accuracy and Efficiency," ICMA Centre Discussion Papers in Finance icma-dp2016-08, Henley Business School, Reading University.
    2. Lena Draeger & Michael J. Lamla, 2015. "Disagreement à la Taylor: Evidence from Survey Microdata," KOF Working papers 15-380, KOF Swiss Economic Institute, ETH Zurich.

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    Keywords

    Macroeconomic expectations; forecasts; noisy information; survey data; disagreement;

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