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Are some forecasters really better than others?

Author Info

  • Antonello D'Agostino
  • Kieran McQuinn
  • Karl Whelan

Abstract

In any dataset with individual forecasts of economic variables, some forecasters will perform better than others. However, it is possible that these ex post differences reflect sampling variation and thus overstate the ex ante differences between forecasters. In this paper, we present a simple test of the null hypothesis that all forecasters in the US Survey of Professional Forecasters have equal ability. We construct a test statistic that reflects both the relative and absolute performance of the forecaster and use bootstrap techniques to compare the empirical results with the equivalents obtained under the null hypothesis of equal forecaster ability. Results suggests limited evidence for the idea that the best forecasters are actually innately better than others, though there is evidence that a relatively small group of forecasters perform very poorly.

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File URL: http://hdl.handle.net/10197/2645
File Function: First version, 2010
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Paper provided by School of Economics, University College Dublin in its series Working Papers with number 201012.

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Date of creation: Apr 2010
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Handle: RePEc:ucn:wpaper:201012
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Keywords: Forecasting; Bootstrap; Economic forecasting--Evaluation; Bootstrap (Statistics);

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Find related papers by JEL classification:
  • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
  • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
  • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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  1. Batchelor, R A, 1990. "All Forecasters Are Equal," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 143-44, January.
  2. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  3. Patton, Andrew J. & Timmermann, Allan, 2007. "Testing Forecast Optimality Under Unknown Loss," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1172-1184, December.
  4. D’Agostino, Antonello & Giannone, Domenico & Surico, Paolo, 2006. "(Un)Predictability and macroeconomic stability," Working Paper Series 0605, European Central Bank.
  5. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, 02.
  6. David H. Romer & Christina D. Romer, 2000. "Federal Reserve Information and the Behavior of Interest Rates," American Economic Review, American Economic Association, vol. 90(3), pages 429-457, June.
  7. Keane, Michael P & Runkle, David E, 1990. "Testing the Rationality of Price Forecasts: New Evidence from Panel Data," American Economic Review, American Economic Association, vol. 80(4), pages 714-35, September.
  8. 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.
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Cited by:

  1. Gamber, Edward N. & Liebner, Jeffrey P. & Smith, Julie K., 2015. "The distribution of inflation forecast errors," Journal of Policy Modeling, Elsevier, vol. 37(1), pages 47-64.
  2. Geoff Kenny & Thomas Kostka & Federico Masera, 2015. "Can Macroeconomists Forecast Risk? Event-Based Evidence from the Euro-Area SPF," International Journal of Central Banking, International Journal of Central Banking, vol. 11(4), pages 1-46, December.
  3. Clements, Michael P, 2012. "Subjective and Ex Post Forecast Uncertainty : US Inflation and Output Growth," The Warwick Economics Research Paper Series (TWERPS) 995, University of Warwick, Department of Economics.
  4. D’Agostino, Antonello & Schnatz, Bernd, 2012. "Survey-based nowcasting of US growth: a real-time forecast comparison over more than 40 years," Working Paper Series 1455, European Central Bank.
  5. Geoff Kenny & Thomas Kostka & Federico Masera, 2015. "Density characteristics and density forecast performance: a panel analysis," Empirical Economics, Springer, vol. 48(3), pages 1203-1231, May.
  6. Tito Nícias Teixeira da Silva Filho, 2013. "Banks, Asset Management or Consultancies' Inflation Forecasts: is there a better forecaster out there?," Working Papers Series 310, Central Bank of Brazil, Research Department.
  7. Constantin Burgi, 2015. "Can A Subset Of Forecasters Beat The Simple Average In The Spf?," Working Papers 2015-001, The George Washington University, Department of Economics, Research Program on Forecasting.
  8. Grothe, Magdalena & Meyler, Aidan, 2015. "Inflation forecasts: Are market-based and survey-based measures informative?," MPRA Paper 66982, University Library of Munich, Germany.
  9. Fabiana Gomez & David Pacini, 2015. "Counting Biased Forecasters: An Application of Multiple Testing Techniques," Bristol Economics Discussion Papers 15/661, Department of Economics, University of Bristol, UK.
  10. Robert W. Rich & Joseph Song & Joseph Tracy, 2012. "The measurement and behavior of uncertainty: evidence from the ECB Survey of Professional Forecasters," Staff Reports 588, Federal Reserve Bank of New York.

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