We seek to extract a small number of representative scenarios from large panel data that are consistent with sample moments. Among two novel algorithms, the first identifies scenarios that have not been observed before, and comes with a scenario-based representation of covariance matrices. The second proposal selects important data points from states of the world that have already realized, and are consistent with higher-order sample moment information. Both algorithms are efficient to compute and lend themselves to consistent scenario-based modeling and multi-dimensional numerical integration that can be used for interpretable decision-making under uncertainty. Extensive numerical benchmarking studies and an application in portfolio optimization favor the proposed algorithms.

翻译：本文旨在从大规模面板数据中提取少量具有代表性的场景，这些场景需与样本矩保持一致。我们提出了两种新颖算法：第一种算法识别以往未观测到的新场景，并提供基于场景的协方差矩阵表示；第二种算法从已实现的世界状态中选择重要数据点，这些数据点与高阶样本矩信息保持一致。两种算法均具有高效的计算特性，适用于构建一致的场景建模与多维数值积分框架，从而支持不确定性下的可解释决策。大量数值基准测试及投资组合优化中的应用实例验证了所提算法的优越性。