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Are Macro-Forecasters Essentially The Same? An Analysis of Disagreement, Accuracy and Efficiency

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  • Michael Clements

    () (ICMA Centre, Henley Business School, University of Reading)

Abstract

We investigate whether there are systematic differences between forecasters in terms of their levels of disagreement and the accuracy of their forecasts, and whether these differences are related to whether or not a forecaster efficiently uses their available information. We ?find that forecasters are not interchangeable. At any point in time, the level of disagreement between forecasters is more likely to be due to a given set of forecasters, as opposed to any randomly-selected set of forecasters. In terms of forecast accuracy, we also fi?nd persistence, in that forecasters who are more (less) accurate in one period tend to be more (less) accurate in a subsequent period. Finally, we reject efficiency for around half of all forecasters at short horizons (depending on the variable in question), and ?find that efficient forecasters tend to be more accurate and less contrarian. Our results do not support the notion that contrarian forecasts stand apart by virtue of having superior information - knowing something that others do not.

Suggested Citation

  • 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.
  • Handle: RePEc:rdg:icmadp:icma-dp2016-08
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    File URL: http://www.henley.ac.uk/files/pdf/exec-ed/ICM-2016-08.pdf
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    References listed on IDEAS

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    1. Ottaviani, Marco & Sorensen, Peter Norman, 2006. "The strategy of professional forecasting," Journal of Financial Economics, Elsevier, vol. 81(2), pages 441-466, August.
    2. Olivier Coibion & Yuriy Gorodnichenko, 2012. "What Can Survey Forecasts Tell Us about Information Rigidities?," Journal of Political Economy, University of Chicago Press, vol. 120(1), pages 116-159.
    3. Andrade, Philippe & Le Bihan, Hervé, 2013. "Inattentive professional forecasters," Journal of Monetary Economics, Elsevier, vol. 60(8), pages 967-982.
    4. Ivana Komunjer & Michael T. Owyang, 2012. "Multivariate Forecast Evaluation and Rationality Testing," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1066-1080, November.
    5. Hendry, David F. & Martinez, Andrew B., 2017. "Evaluating multi-step system forecasts with relatively few forecast-error observations," International Journal of Forecasting, Elsevier, vol. 33(2), pages 359-372.
    6. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    7. 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.
    8. repec:taf:jnlbes:v:30:y:2012:i:1:p:1-17 is not listed on IDEAS
    9. Antonello D’Agostino & Kieran Mcquinn & Karl Whelan, 2012. "Are Some Forecasters Really Better Than Others?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(4), pages 715-732, June.
    10. Andrade, Philippe & Crump, Richard K. & Eusepi, Stefano & Moench, Emanuel, 2016. "Fundamental disagreement," Journal of Monetary Economics, Elsevier, vol. 83(C), pages 106-128.
    11. Michael P. Clements, 2014. "Forecast Uncertainty- Ex Ante and Ex Post : U.S. Inflation and Output Growth," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 206-216, April.
    12. Ball, Laurence & Jalles, João Tovar & Loungani, Prakash, 2015. "Do forecasters believe in Okun’s Law? An assessment of unemployment and output forecasts," International Journal of Forecasting, Elsevier, vol. 31(1), pages 176-184.
    13. Whelan, Karl, 2003. " A Two-Sector Approach to Modeling U.S. NIPA Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 35(4), pages 627-656, August.
    14. Rich, Robert W & Butler, J S, 1998. "Disagreement as a Measure of Uncertainty: A Comment on Bomberger," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 30(3), pages 411-419, August.
    15. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
    16. Lamont, Owen A., 2002. "Macroeconomic forecasts and microeconomic forecasters," Journal of Economic Behavior & Organization, Elsevier, vol. 48(3), pages 265-280, July.
    17. Davies, Anthony & Lahiri, Kajal, 1995. "A new framework for analyzing survey forecasts using three-dimensional panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 205-227, July.
    18. L. R. Klein & R. F. Kosobud, 1961. "Some Econometrics of Growth: Great Ratios of Economics," The Quarterly Journal of Economics, Oxford University Press, vol. 75(2), pages 173-198.
    19. David Laster & Paul Bennett & In Sun Geoum, 1999. "Rational Bias in Macroeconomic Forecasts," The Quarterly Journal of Economics, Oxford University Press, vol. 114(1), pages 293-318.
    20. Olivier Coibion & Yuriy Gorodnichenko, 2015. "Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts," American Economic Review, American Economic Association, vol. 105(8), pages 2644-2678, August.
    21. Clements, Michael P., 2016. "Long-run restrictions and survey forecasts of output, consumption and investment," International Journal of Forecasting, Elsevier, vol. 32(3), pages 614-628.
    22. 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.
    23. 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.
    24. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    25. J. Steven Landefeld & Eugene P. Seskin & Barbara M. Fraumeni, 2008. "Taking the Pulse of the Economy: Measuring GDP," Journal of Economic Perspectives, American Economic Association, vol. 22(2), pages 193-216, Spring.
    26. 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.
    27. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    28. 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.
    29. Zarnowitz, Victor & Lambros, Louis A, 1987. "Consensus and Uncertainty in Economic Prediction," Journal of Political Economy, University of Chicago Press, vol. 95(3), pages 591-621, June.
    30. Clements, Michael P, 1995. "Rationality and the Role of Judgement in Macroeconomic Forecasting," Economic Journal, Royal Economic Society, vol. 105(429), pages 410-420, March.
    31. Chanont Banternghansa & Michael W. McCracken, 2009. "Forecast disagreement among FOMC members," Working Papers 2009-059, Federal Reserve Bank of St. Louis.
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    More about this item

    Keywords

    Expectations formation; Disagreement; Accuracy; Forecast Efficiency;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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