This paper presents an algorithm that generates the conditional moment inequalities that characterize the identified set of the common parameter of various semi-parametric panel multinomial choice models. I consider both static and dynamic models, and consider various weak stochastic restrictions on the distribution of observed and unobserved components of the models. For a broad class of such stochastic restrictions, the paper demonstrates that the inequalities characterizing the identified set of the common parameter can be obtained as solutions of multiple objective linear programs (MOLPs), thereby transforming the task of finding these inequalities into a purely computational problem. The algorithm that I provide reproduces many well-known results, including the conditional moment inequalities derived in Manski 1987, Pakes and Porter 2023, and Khan, Ponomareva, and Tamer 2023. Moreover, I use the algorithm to generate some new results, by providing characterizations of the identified set in some cases that were left open in Pakes and Porter 2023 and Khan, Ponomareva, and Tamer 2023, as well as characterizations of the identified set under alternative stochastic restrictions.

翻译：本文提出了一种算法，可生成刻画多种半参数面板多项选择模型中共同参数识别集的条件矩不等式。我同时考虑了静态与动态模型，并针对模型可观测与不可观测成分的分布施加了多种弱随机约束。对于一大类此类随机约束，本文证明刻画共同参数识别集的不等式可作为多目标线性规划（MOLP）的解获得，从而将求解这些不等式的任务转化为纯粹的计算问题。该算法复现了众多经典结论，包括Manski（1987）、Pakes与Porter（2023）以及Khan、Ponomareva与Tamer（2023）提出的条件矩不等式。此外，我利用该算法生成了新成果：一方面对Pakes与Porter（2023）及Khan、Ponomareva与Tamer（2023）中未解决的某些情形提供了识别集的刻画，另一方面给出了替代性随机约束下识别集的表征。