论文标题
基于得分的校准测试用于多元预测分布
Score-based calibration testing for multivariate forecast distributions
论文作者
论文摘要
基于概率积分变换（PIT）的校准测试通常用于评估单变量分布预测的质量。但是，基于坑的多元分布预测的基于坑的校准测试面临着各种挑战。我们根据适当的评分规则提出了两种新的测试，这些测试克服了这些挑战。它们源于本工作中引入的多元案例中校准测试的一般框架。新测试在模拟中具有良好的尺寸和功率特性，并解决了现有测试的各种问题。我们将测试应用于宏观经济和财务时间序列数据的预测分布。
Calibration tests based on the probability integral transform (PIT) are routinely used to assess the quality of univariate distributional forecasts. However, PIT-based calibration tests for multivariate distributional forecasts face various challenges. We propose two new types of tests based on proper scoring rules, which overcome these challenges. They arise from a general framework for calibration testing in the multivariate case, introduced in this work. The new tests have good size and power properties in simulations and solve various problems of existing tests. We apply the tests to forecast distributions for macroeconomic and financial time series data.
