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Scoring rules and survey density forecasts

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  • 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. Carlos Capistrán & Allan Timmermann, 2006. "Forecast Combination with Entry and Exit of Experts," Working Papers 2006-08, Banco de México.
  2. 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.
  3. Engelberg, Joseph & Manski, Charles F. & Williams, Jared, 2009. "Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 30-41.
  4. 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.
  5. Gianna Boero & Jeremy Smith & KennethF. Wallis, 2008. "Uncertainty and Disagreement in Economic Prediction: The Bank of England Survey of External Forecasters," Economic Journal, Royal Economic Society, vol. 118(530), pages 1107-1127, 07.
  6. 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.
  7. Gianni Amisano & Raffaella Giacomini, 2005. "Comparing Density Forecsts via Weighted Likelihood Ratio Tests," Working Papers ubs0504, University of Brescia, Department of Economics.
  8. 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.
  9. John Galbraith & Simon van Norden, 2009. "Calibration and Resolution Diagnostics for Bank of England Density Forecasts," CIRANO Working Papers 2009s-36, CIRANO.
  10. 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.
  11. Gianna Boero & Jeremy Smith & Kenneth F. Wallis, 2008. "Here is the News: Forecast Revisions in the Bank of England Survey of External Forecasters," National Institute Economic Review, National Institute of Economic and Social Research, vol. 203(1), pages 68-77, January.
  12. 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.
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Cited by:
  1. 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.
  2. 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.
  3. repec:syb:wpbsba:01/2013 is not listed on IDEAS
  4. Jason Ng & Catherine S. Forbes & Gael M. Martin & Brendan P.M. McCabe, 2011. "Non-Parametric Estimation of Forecast Distributions in Non-Gaussian, Non-linear State Space Models," Monash Econometrics and Business Statistics Working Papers 11/11, Monash University, Department of Econometrics and Business Statistics.
  5. Tsyplakov, Alexander, 2014. "Theoretical guidelines for a partially informed forecast examiner," MPRA Paper 55017, University Library of Munich, Germany.

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