Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5096586 | Journal of Econometrics | 2011 | 16 Pages |
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 toward such densities. Using our novel likelihood-based scoring rules avoids this problem.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Cees Diks, Valentyn Panchenko, Dick van Dijk,