标题： 密度预测转换 显示英文标题

标题： Density forecast transformations

摘要： 使用$direct$预测方案是常见的选择，这意味着个体预测不包含关于跨期限依赖性的信息。 然而，如果预测者需要根据$direct$密度预测构建依赖于多个期限的预测对象（例如，当从季度增长率构建年度平均增长率时，$e.g.$），则需要这种依赖性。 为了解决这个问题，我们建议使用置信函数将各个$h$步 ahead 的预测分布组合成一个联合预测分布。 对于那些改变$direct$预测规范成本过高的从业者来说，我们的方法特别有吸引力。 在蒙特卡洛研究中，我们证明了我们的方法比忽略潜在依赖性的方法更能准确地逼近真实密度。 我们展示了所提出方法在多个实证例子中的优越性能，其中我们构建了（i）使用月度对月度$direct$预测的季度预测，（ii）使用月度对年度$direct$预测的年度平均预测，以及（iii）使用季度对季度$direct$预测的年度平均预测。 显示英文摘要

摘要： The popular choice of using a $direct$ forecasting scheme implies that the individual predictions do not contain information on cross-horizon dependence. However, this dependence is needed if the forecaster has to construct, based on $direct$ density forecasts, predictive objects that are functions of several horizons ($e.g.$ when constructing annual-average growth rates from quarter-on-quarter growth rates). To address this issue we propose to use copulas to combine the individual $h$-step-ahead predictive distributions into a joint predictive distribution. Our method is particularly appealing to practitioners for whom changing the $direct$ forecasting specification is too costly. In a Monte Carlo study, we demonstrate that our approach leads to a better approximation of the true density than an approach that ignores the potential dependence. We show the superior performance of our method in several empirical examples, where we construct (i) quarterly forecasts using month-on-month $direct$ forecasts, (ii) annual-average forecasts using monthly year-on-year $direct$ forecasts, and (iii) annual-average forecasts using quarter-on-quarter $direct$ forecasts.