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Scoring rules and survey density forecasts

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Author Info

  • Boero, Gianna
  • Smith, Jeremy
  • Wallis, Kenneth F.

Abstract

This article provides a practical evaluation of some leading density forecast scoring rules in the context of forecast surveys. We analyse the density forecasts of UK inflation obtained from the Bank of England's Survey of External Forecasters, considering both the survey average forecasts published in the Bank's quarterly Inflation Report, and the individual survey responses recently made available to researchers by the Bank. The density forecasts are collected in histogram format, and the ranked probability score (RPS) is shown to have clear advantages over other scoring rules. Missing observations are a feature of forecast surveys, and we introduce an adjustment to the RPS, based on the Yates decomposition, to improve its comparative measurement of forecaster performance in the face of differential non-response. The new measure, denoted RPS*, is recommended to analysts of forecast surveys.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 27 (2011)
Issue (Month): 2 (April)
Pages: 379-393

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Handle: RePEc:eee:intfor:v:27:y::i:2:p:379-393

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Web page: http://www.elsevier.com/locate/ijforecast

Related research

Keywords: Density forecast evaluation Brier (quadratic probability) score Epstein (ranked probability) score Logarithmic score Bank of England Survey of External Forecasters Missing data Forecast comparison;

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References

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  1. Boero,Gianna & Smith,Jeremy & Wallis,Kenneth F, 2006. "Uncertainty and disagreement in economic prediction : the Bank of England Survey of External Forecasters," The Warwick Economics Research Paper Series (TWERPS) 811, University of Warwick, Department of Economics.
  2. Capistrán, Carlos & Timmermann, Allan, 2009. "Forecast Combination With Entry and Exit of Experts," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 428-440.
  3. Giacomini, Raffaella & White, Halbert, 2003. "Tests of Conditional Predictive Ability," University of California at San Diego, Economics Working Paper Series qt5jk0j5jh, Department of Economics, UC San Diego.
  4. Boero, Gianna & Smith, Jeremy & Wallis, Kenneth F., 2008. "Evaluating a three-dimensional panel of point forecasts: The Bank of England Survey of External Forecasters," International Journal of Forecasting, Elsevier, vol. 24(3), pages 354-367.
  5. Amisano, Gianni & Giacomini, Raffaella, 2007. "Comparing Density Forecasts via Weighted Likelihood Ratio Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 177-190, April.
  6. Groen, Jan J.J. & Kapetanios, George & Price, Simon, 2009. "A real time evaluation of Bank of England forecasts of inflation and growth," International Journal of Forecasting, Elsevier, vol. 25(1), pages 74-80.
  7. Joseph Engelberg & Charles F. Manski & Jared Williams, 2006. "Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters," NBER Working Papers 11978, National Bureau of Economic Research, Inc.
  8. Casillas-Olvera, Gabriel & Bessler, David A., 2006. "Probability forecasting and central bank accountability," Journal of Policy Modeling, Elsevier, vol. 28(2), pages 223-234, February.
  9. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
  10. John Galbraith & Simon van Norden, 2009. "Calibration and Resolution Diagnostics for Bank of England Density Forecasts," CIRANO Working Papers 2009s-36, CIRANO.
  11. Yates, J. Frank, 1988. "Analyzing the accuracy of probability judgments for multiple events: An extension of the covariance decomposition," Organizational Behavior and Human Decision Processes, Elsevier, vol. 41(3), pages 281-299, June.
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Cited by:
  1. Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013. "Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
  2. Jan-Egbert Sturm & Jakob de Haan, 2009. "Does Central Bank Communication really Lead to better Forecasts of Policy Decisions? New Evidence Based on a Taylor Rule Model for the ECB," CESifo Working Paper Series 2760, CESifo Group Munich.
  3. Tsyplakov, Alexander, 2014. "Theoretical guidelines for a partially informed forecast examiner," MPRA Paper 55017, University Library of Munich, Germany.
  4. Tim Oliver Berg & Steffen Henzel, 2013. "Point and Density Forecasts for the Euro Area Using Many Predictors: Are Large BVARs Really Superior?," Ifo Working Paper Series Ifo Working Paper No. 155, Ifo Institute for Economic Research at the University of Munich.
  5. repec:syb:wpbsba:01/2013 is not listed on IDEAS

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