用支付网络分析揭示经济隐性关联，提升实时监测精度。
Network Structure in UK Payment Flows: Evidence on Economic Interdependencies and Implications for Real-Time Measurement
- 构建支付网络图谱，用中心性与聚类系数预测现金流
- 疫情中预测准确率提升13.8个百分点，传统方法下降明显
- 金融、批发、专业服务为关键枢纽，适合政策与风控参考
对2017至2024年英国89个行业间的532,346笔支付记录进行网络分析，发现传统双边统计无法捕捉的结构性经济关系。基于图论特征（如中心性、聚类系数）的模型使支付流预测准确率较传统时间序列方法提升8.8个百分点。尤其在新冠疫情冲击期间，传统模型预测效能大幅下滑（R²从0.38降至0.19），而网络增强模型仍保持高精度，贡献达+13.8个百分点。金融服务业、批发贸易和专业服务业被识别为结构核心行业，其网络地位反映系统重要性远超交易量。样本期内网络密度整体上升12.5%，2020年出现显著中断后恢复并超越疫情前整合水平。结果表明，支付网络监控可作为官方统计数据的领先指标，助力结构性经济变化的早期识别，并在传统时序模式失效时提升实时预估准确性。
原文摘要 · Abstract (English)
Network analysis of inter-industry payment flows reveals structural economic relationships invisible to traditional bilateral measurement approaches, with significant implications for real-time economic monitoring. Analysing 532,346 UK payment records (2017--2024) across 89 industry sectors, we demonstrate that graph-theoretic features which include centrality measures and clustering coefficients improve payment flow forecasting by 8.8 percentage points beyond traditional time-series methods. Critically, network features prove most valuable during economic disruptions: during the COVID-19 pandemic, when traditional forecasting accuracy collapsed (R2} falling from 0.38 to 0.19), network-enhanced models maintained substantially better performance, with network contributions reaching +13.8 percentage points. The analysis identifies Financial Services, Wholesale Trade, and Professional Services as structurally central industries whose network positions indicate systemic importance beyond their transaction volumes. Network density increased 12.5\% over the sample period, with visible disruption during 2020 followed by recovery exceeding pre-pandemic integration levels. These findings suggest payment network monitoring could enhance official statistics production by providing leading indicators of structural economic change and improving nowcasting accuracy during periods when traditional temporal patterns prove unreliable.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。