Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails
نویسندگان
چکیده
We propose new scoring rules based on partial 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 are proper and 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 partial likelihood-based scoring rules avoids this problem.
منابع مشابه
Likelihood-based scoring rules for comparing density forecasts in tails
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 riskmanagement. 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...
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