论文标题
证明预测
Proving prediction prudence
论文作者
论文摘要
我们研究如何对成对的观测和预测样本进行测试，以评估预测是否谨慎。审慎要求观察预测对差异的平均值可以证明是显着负面的。对于安全的结论，我们建议测试未加权（或同样加权）和加权手段，并明确考虑单个对的随机性。提出的测试方法主要指定为引导程序和正常近似算法。这些测试是一般的，但可以特别适用于信用风险领域，无论是用于监管和会计目的。
We study how to perform tests on samples of pairs of observations and predictions in order to assess whether or not the predictions are prudent. Prudence requires that that the mean of the difference of the observation-prediction pairs can be shown to be significantly negative. For safe conclusions, we suggest testing both unweighted (or equally weighted) and weighted means and explicitly taking into account the randomness of individual pairs. The test methods presented are mainly specified as bootstrap and normal approximation algorithms. The tests are general but can be applied in particular in the area of credit risk, both for regulatory and accounting purposes.
