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Likelihood-based scoring rules for comparing density forecasts in tails

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Author Info

  • Cees Diks

    ()
    (Department of Quantitive Economics - University of Amsterdam)

  • Valentyn Panchenko

    ()
    (Faculty of Business - University of New South Wales)

  • Dick van Dijk

    ()
    (Erasmus University Rotterdam - Erasmus University Rotterdam)

Abstract

We propose new scoring rules based on conditional and censored likelihood for assessing the predictive accuracy of competing density forecasts over a specific region of interest, such as the left tail in financial risk management. These scoring rules can be interpreted in terms of Kullback-Leibler divergence between weighted versions of the density forecast and the true density. Existing scoring rules based on weighted likelihood favor density forecasts with more probability mass in the given region, rendering predictive accuracy tests biased towards such densities. Using our novel likelihood-based scoring rules avoids this problem.

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Bibliographic Info

Paper provided by HAL in its series Post-Print with number peer-00834423.

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Date of creation: 15 Jun 2011
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Publication status: Published, Journal of Econometrics, 2011
Handle: RePEc:hal:journl:peer-00834423

Note: View the original document on HAL open archive server: http://peer.ccsd.cnrs.fr/peer-00834423
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Related research

Keywords: C12; C22; C52; C53; Density forecast evaluation; Scoring rules; Weighted likelihood ratio scores; Conditional likelihood; Censored likelihood; Risk management;

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References

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Citations

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Cited by:
  1. Götz Thomas B. & Hecq Alain & Urbain Jean-Pierre, 2012. "Real-Time Forecast Density Combinations (Forecasting US GDP Growth Using Mixed-Frequency Data)," Research Memorandum 021, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  2. Barbara Rossi & Tatevik Sehkposyan, 2013. "Evaluating Predictive Densities of U.S. Output Growth and Inflation in a Large Macroeconomic Data Set," Working Papers 689, Barcelona Graduate School of Economics.
  3. Rossi, Barbara & Sekhposyan, Tatevik, 2013. "Conditional predictive density evaluation in the presence of instabilities," Journal of Econometrics, Elsevier, vol. 177(2), pages 199-212.
  4. Tsyplakov, Alexander, 2014. "Theoretical guidelines for a partially informed forecast examiner," MPRA Paper 55017, University Library of Munich, Germany.
  5. Cees Diks & Valentyn Panchenko & Oleg Sokolinskiy & Dick van Dijk, 2013. "Comparing the Accuracy of Copula-Based Multivariate Density Forecasts in Selected Regions of Support," Tinbergen Institute Discussion Papers 13-061/III, Tinbergen Institute.
  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. Lukasz Gatarek & Lennart Hoogerheide & Koen Hooning & Herman K. van Dijk, 2013. "Censored Posterior and Predictive Likelihood in Left-Tail Prediction for Accurate Value at Risk Estimation," Tinbergen Institute Discussion Papers 13-060/III, Tinbergen Institute, revised 06 Mar 2014.
  8. repec:syb:wpbsba:01/2013 is not listed on IDEAS
  9. Rossi, Barbara & Sekhposyan, Tatevik, 2014. "Evaluating predictive densities of US output growth and inflation in a large macroeconomic data set," International Journal of Forecasting, Elsevier, vol. 30(3), pages 662-682.
  10. Mark J. Jensen & John M. Maheu, 2012. "Bayesian semiparametric multivariate GARCH modeling," Working Paper 2012-09, Federal Reserve Bank of Atlanta.
  11. repec:dgr:uvatin:2013060 is not listed on IDEAS
  12. repec:dgr:uvatin:2013061 is not listed on IDEAS
  13. N. Fawcett & G. Kapetanios & J. Mitchell & S. Price, 2014. "Generalised Density Forecast Combinations," CAMA Working Papers 2014-24, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

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