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Likelihood-based scoring rules for comparing density forecasts in tails
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DOI：10.1016/j.jeconom.2011.04.001.png)
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Abstract
En 中文
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. (C) 2011 Elsevier B.V. All rights reserved.
Keywords:
Density forecast evaluation
Scoring rules
Weighted likelihood ratio scores
Conditional likelihood
Censored likelihood
Risk management
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