面向空间因子模型的Wasserstein重心交互场：来自语言模型表征的证据

摘要

空间收益模型将交互矩阵视为给定，不对反馈进行解释。我们利用目标锚定的Wasserstein重心重建，从企业语言模型文章嵌入分布构建出一个无带宽场。通过二次暴露调整问题，反馈被映射为同行错位惩罚比率。对52家企业而言，由2018–2022年新闻冻结而得的该场，给出2023–2026年惩罚比率3.46（95%区间[2.89, 4.17]），且其条件拟似然高于等权重同行支持或对相同距离采用RBF加权的情形。重心场与新闻共现场的联合惩罚比率分别为2.33与0.86，对应的边界校准检验拒绝两者的排除设定。

English

Spatial return models take the interaction matrix as given and leave feedback uninterpreted. We construct a bandwidth-free field from firms' language-model article embedding distributions using target-anchored Wasserstein barycentric reconstruction. A quadratic exposure-adjustment problem maps feedback into a peer-misalignment penalty ratio. For 52 firms, the field, frozen from 2018-2022 news, yields a 2023-2026 penalty ratio of 3.46 (95% interval [2.89, 4.17]) and higher conditional quasi-likelihood than equal-weighted peer support or RBF weighting of the same distances. Joint penalty ratios for the barycentric and news co-mention fields are 2.33 and 0.86 with boundary calibrated tests which reject both exclusions.