Model diagnostics and forecast evaluation are closely related tasks, with the former concerning in-sample goodness (or lack) of fit and the latter addressing predictive performance out-of-sample. We review the ubiquitous setting in which forecasts are cast in the form of quantiles or quantile-bounded prediction intervals. We distinguish unconditional calibration, which corresponds to classical coverage criteria, from the stronger notion of conditional calibration, as can be visualized in quantile reliability diagrams. Consistent scoring functions—including, but not limited to, the widely used asymmetricpiecewise linear score or pinball loss—provide for comparative assessment and ranking, and link to the coefficient of determination and skill scores. We illustrate the use of these tools on Engel's food expenditure data, the Global Energy Forecasting Competition 2014, and the US COVID-19 Forecast Hub.

中文翻译：

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分位数的模型诊断和预测评估

模型诊断和预测评估是密切相关的任务，前者涉及样本内的拟合优度（或缺乏），后者解决样本外的预测性能。我们回顾了以分位数或分位数边界预测区间的形式进行预测的普遍设置。我们将对应于经典覆盖标准的无条件校准与条件校准的更强概念区分开来，如分位数可靠性图所示。一致的评分函数（包括但不限于广泛使用的不对称分段线性评分或弹球损失）提供比较评估和排名，并与确定系数和技能评分相关联。我们在 Engel 的食品支出数据、2014 年全球能源预测竞赛和美国 COVID-19 预测中心上说明了这些工具的使用。