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Consensus Forecasts and Inefficient Information Aggregation

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  • Mr. Christopher W. Crowe

Abstract

Consensus forecasts are inefficient, over-weighting older information already in the public domain at the expense of new private information, when individual forecasters have different information sets. Using a cross-country panel of growth forecasts and new methodological insights, this paper finds that: consensus forecasts are inefficient as predicted; this is not due to individual forecaster irrationality; forecasters appear unaware of this inefficiency; and a simple adjustment reduces forecast errors by 5 percent. Similar results are found using US nominal GDP forecasts. The paper also discusses the result’s implications for users of forecaster surveys and for the literature on information aggregation.

Suggested Citation

  • Mr. Christopher W. Crowe, 2010. "Consensus Forecasts and Inefficient Information Aggregation," IMF Working Papers 2010/178, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2010/178
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    References listed on IDEAS

    as
    1. Batchelor, Roy & Dua, Pami, 1991. "Blue Chip Rationality Tests," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 23(4), pages 692-705, November.
    2. Bonham, Carl S & Cohen, Richard H, 2001. "To Aggregate, Pool, or Neither: Testing the Rational-Expectations Hypothesis Using Survey Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(3), pages 278-291, July.
    3. 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.
    4. Carl S Bonham & Richard H Cohen, 2000. "Testing the Rational Expectations Hypothesis using Survey Data," Working Papers 200007, University of Hawaii at Manoa, Department of Economics.
    5. Jeffery Amato & Hyun Shin, 2006. "Imperfect common knowledge and the information value of prices," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 27(1), pages 213-241, January.
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    Citations

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    Cited by:

    1. Constantin Bürgi & Julio L. Ortiz, 2022. "Overreaction through Anchoring," CESifo Working Paper Series 10193, CESifo.
    2. Frank A. G. den Butter & Pieter W. Jansen, 2013. "Beating the random walk: a performance assessment of long-term interest rate forecasts," Applied Financial Economics, Taylor & Francis Journals, vol. 23(9), pages 749-765, May.
    3. Jeffrey C. Fuhrer, 2018. "Intrinsic expectations persistence: evidence from professional and household survey expectations," Working Papers 18-9, Federal Reserve Bank of Boston.
    4. Gamber, Edward N. & Smith, Julie K. & McNamara, Dylan C., 2014. "Where is the Fed in the distribution of forecasters?," Journal of Policy Modeling, Elsevier, vol. 36(2), pages 296-312.
    5. Michael P. Clements, 2022. "Forecaster Efficiency, Accuracy, and Disagreement: Evidence Using Individual‐Level Survey Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(2-3), pages 537-568, March.
    6. Artūras Juodis & Simas Kučinskas, 2023. "Quantifying noise in survey expectations," Quantitative Economics, Econometric Society, vol. 14(2), pages 609-650, May.
    7. Reslow, André, 2019. "Inefficient Use of Competitors'Forecasts?," Working Paper Series 380, Sveriges Riksbank (Central Bank of Sweden).
    8. Mr. Marcos Poplawski Ribeiro & Jan-Christoph Rülke, 2011. "Fiscal Expectations Under the Stability and Growth Pact: Evidence from Survey Data," IMF Working Papers 2011/048, International Monetary Fund.
    9. Kenneth Wallis, 2011. "Combining forecasts - forty years later," Applied Financial Economics, Taylor & Francis Journals, vol. 21(1-2), pages 33-41.
    10. Dovern, Jonas & Fritsche, Ulrich & Loungani, Prakash & Tamirisa, Natalia, 2013. "Information Rigidities in Economic Growth Forecasts: Evidence from a Large International Panel," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79936, Verein für Socialpolitik / German Economic Association.
    11. 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.
    12. 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.
    13. Trabelsi, Emna, 2016. "Central bank transparency and the consensus forecast: What does The Economist poll of forecasters tell us?," Research in International Business and Finance, Elsevier, vol. 38(C), pages 338-359.
    14. Alfredo Pistelli M., 2012. "Análisis de Sesgos y Eficiencia en Proyecciones de Consensus Forecasts," Notas de Investigación Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 15(1), pages 98-104, April.

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