空間因子模型的 Wasserstein 重心交互作用場：來自語言模型表徵的證據

摘要

空間收益模型將交互矩陣視為已知，且不對回饋進行詮釋。我們利用目標錨定的Wasserstein重心重建，從公司的語言模型文章嵌入分佈中建構一個無頻寬的場域。一個二次曝險調整問題將回饋映射為同儕錯位懲罰比率。針對52家公司，該場域以2018–2022年新聞凍結，得出2023–2026年的懲罰比率為3.46（95%信賴區間[2.89, 4.17]），且條件準似然高於等權重同儕支持或相同距離的RBF加權。重心場域與新聞共現場域的聯合懲罰比率分別為2.33和0.86，邊界校正檢定拒絕剔除兩者。

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.