Separating Time-Varying Network Composition from Predictive Dependence under Noisy Network Measurement
含噪网络测量下时变网络构成与预测依赖的分离AI 解读 ↓
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含噪网络测量下时变网络构成与预测依赖的分离
- 研究问题
- 当网络潜在、随时间变化且测量含噪时，如何区分结果变化来自连接构成变化还是预测依赖强度变化，避免误读回归系数？
- 方法与思路
- 作者联合建模结果与网络的重复含噪测量，建立局部识别条件，并构造强度和构成路径估计、置信集合及变化归因检验。
- 材料陈述的贡献
- 摘要称给出局部识别的充要条件、弱识别下仍精确的置信集合和共同报告偏差下的界限，并以十八个经济体贸易面板展示应用。
- 阅读关注点
- 应核对重复测量的误差结构、强度与构成的参数化、精确推断成立的条件，以及共同报告偏差和弱识别的诊断方式。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；目标是预测依赖，不能解释为因果效应，具体识别条件与实证稳健性未说明。
原文摘要
A common question about networked time series is whether outcomes changed because shocks transmit more strongly or because the pattern of connections changed. Standard practice inserts a recorded network into an outcome regression and reads movements of the fitted coefficient as changes in transmission strength. When the network is latent, time varying, and measured with error, this reading fails: changes in strength and changes in composition can produce the same outcome distribution at a single date, and the population coefficient moves under composition changes alone. The question becomes answerable when outcomes are analyzed jointly with repeated noisy measurements of the network, such as paired reports of bilateral trade flows. For the joint model we establish necessary and sufficient conditions for local identification, estimators of the strength and composition paths, a simultaneous confidence band for the strength path, confidence sets that remain exact under weak identification, breakdown bounds under common reporting bias, and an exactly sized test that detects changes on the observed path and attributes them to strength or to composition. Simulations assess each procedure at its stated boundary. On a mirror-reported trade panel of eighteen economies over 1995 to 2020, the diagnostics flag exactly the crisis years and the composition coordinate attached to European Union membership declines by roughly two thirds. The estimand is predictive dependence, not a causal effect.