We seek to extract a small number of representative scenarios from large and high-dimensional 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 picks 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 high-dimensional numerical integration. Extensive numerical benchmarking studies and an application in portfolio optimization favor the proposed algorithms.

翻译：我们旨在从大规模高维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一致。在两种新颖算法中，第一种可识别此前未观测到的场景，并提供了基于场景的协方差矩阵表示方法。第二种方法则从已实现的世界状态中选取重要数据点，且这些数据点与高阶样本矩信息一致。两种算法均计算高效，适用于一致的基于场景建模与高维数值积分。广泛的数值基准测试研究及投资组合优化应用表明，所提算法具有显著优势。