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Kernel-based calibration diagnostics for recession and inflation probability forecasts


  • Galbraith, John W.
  • van Norden, Simon


A probabilistic forecast is the estimated probability with which a future event will occur. One interesting feature of such forecasts is their calibration, or the match between the predicted probabilities and the actual outcome probabilities. Calibration has been evaluated in the past by grouping probability forecasts into discrete categories. We show here that we can do this without discrete groupings; the kernel estimators that we use produce efficiency gains and smooth estimated curves relating the predicted and actual probabilities. We use such estimates to evaluate the empirical evidence on the calibration error in a number of economic applications, including the prediction of recessions and inflation, using both forecasts made and stored in real time and pseudo-forecasts made using the data vintage available at the forecast date. The outcomes are evaluated using both first-release outcome measures and subsequent revised data. We find substantial evidence of incorrect calibration in professional forecasts of recessions and inflation from the SPF, as well as in real-time inflation forecasts from a variety of output gap models.

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  • Galbraith, John W. & van Norden, Simon, 2011. "Kernel-based calibration diagnostics for recession and inflation probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1041-1057, October.
  • Handle: RePEc:eee:intfor:v:27:y:2011:i:4:p:1041-1057

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    References listed on IDEAS

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    Cited by:

    1. Chris McDonald & Craig Thamotheram & Shaun P. Vahey & Elizabeth C. Wakerly, 2016. "Assessing the economic value of probabilistic forecasts in the presence of an inflation target," Reserve Bank of New Zealand Discussion Paper Series DP2016/10, Reserve Bank of New Zealand.
    2. Simon van Norden, 2015. "Estimates of Québec’s Growth Uncertainty," CIRANO Project Reports 2015rp-01, CIRANO.
    3. Lahiri, Kajal & Peng, Huaming & Zhao, Yongchen, 2015. "Testing the value of probability forecasts for calibrated combining," International Journal of Forecasting, Elsevier, vol. 31(1), pages 113-129.
    4. Lahiri, Kajal & Wang, J. George, 2013. "Evaluating probability forecasts for GDP declines using alternative methodologies," International Journal of Forecasting, Elsevier, vol. 29(1), pages 175-190.
    5. Turgut Kisinbay & Chikako Baba, 2011. "Predicting Recessions; A New Approach for Identifying Leading Indicators and Forecast Combinations," IMF Working Papers 11/235, International Monetary Fund.
    6. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters,in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8 Bank for International Settlements.
    7. McDonald, Christopher & Thamotheram, Craig & Vahey, Shaun P. & Wakerly, Elizabeth C., 2015. "Assessing the Economic Value of Probabilistic Forecasts in the Presence of an Inflation Target," EMF Research Papers 09, Economic Modelling and Forecasting Group.
    8. Tsyplakov, Alexander, 2014. "Theoretical guidelines for a partially informed forecast examiner," MPRA Paper 55017, University Library of Munich, Germany.
    9. Tsyplakov, Alexander, 2013. "Evaluation of Probabilistic Forecasts: Proper Scoring Rules and Moments," MPRA Paper 45186, University Library of Munich, Germany.
    10. Tsyplakov, Alexander, 2012. "Assessment of probabilistic forecasts: Proper scoring rules and moments," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 27(3), pages 115-132.


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