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The Calibration Of Probabilistic Economic Forecasts

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John Galbraith ()
Simon van Norden ()

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Abstract

A probabilistic forecast is the estimated probability with which a future event will satisfy a particular criterion. One interesting feature of such forecasts is their calibration, or the match between predicted probabilities and actual outcome probabilities. Calibration has been evaluated in the past by gropuing probability forecasts into discrete categories. Here we show that we can do so without discrete groupings; the kernel estimators that we use produce efficiency gains and smooth estimated curves relating predicted and actual probabilities. We use such estimates to evaluate the empirical evidence on calibration error in a number of economic applications including recession and inflation prediction, using both forecasts made and stored in real time and pseudo-forecasts made using the data vintage available at the forecast date. We evaluate outcomes using both first-release outcome measures as well as later, thoroughly revised data. We find strong evidence of incorrect calibration in professional forecasts of recessions and inflation. We also present evidence of asymmetries in the performace of inflation forecasts based on real-time output gaps.

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Paper provided by McGill University, Department of Economics in its series Departmental Working Papers with number 2008-05.

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Length: 31 pages
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Handle: RePEc:mcl:mclwop:2008-05

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  1. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268. [Downloadable!] (restricted)
  2. Dean Croushore & Tom Stark, 2003. "A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 605-617, 04. [Downloadable!] (restricted)
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  3. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March. [Downloadable!] (restricted)
  4. Watson, Mark W., 1986. "Univariate detrending methods with stochastic trends," Journal of Monetary Economics, Elsevier, vol. 18(1), pages 49-75, July. [Downloadable!] (restricted)
  5. Enno Mammen, . "Comparing nonparametric versus parametric regression fits," Statistic und Oekonometrie 9205, Humboldt Universitaet Berlin. [Downloadable!]
  6. Athanasios Orphanides & Simon van Norden, 2002. "The Unreliability of Output-Gap Estimates in Real Time," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 569-583, 07. [Downloadable!] (restricted)
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  7. Linton, O. & Whang, Yoon-Jae, 2007. "The quantilogram: With an application to evaluating directional predictability," Journal of Econometrics, Elsevier, vol. 141(1), pages 250-282, November. [Downloadable!] (restricted)
  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. [Downloadable!] (restricted)
  9. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S119-36, Suppl. De. [Downloadable!] (restricted)
  10. Orphanides, Athanasios & van Norden, Simon, 2005. "The Reliability of Inflation Forecasts Based on Output Gap Estimates in Real Time," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 583-601, June.
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  11. Diebold, Francis X & Rudebusch, Glenn D, 1989. "Scoring the Leading Indicators," Journal of Business, University of Chicago Press, vol. 62(3), pages 369-91, July. [Downloadable!] (restricted)
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  12. John W. Galbraith & Greg Tkacz, 2007. "Forecast Content And Content Horizons For Some Important Macroeconomic Time Series," Departmental Working Papers 2007-01, McGill University, Department of Economics. [Downloadable!]
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  13. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May. [Downloadable!] (restricted)
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  14. Glenn D. Rudebusch & John C. Williams, 2007. "Forecasting recessions: the puzzle of the enduring power of the yield curve," Working Paper Series 2007-16, Federal Reserve Bank of San Francisco. [Downloadable!]
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