发表机构
Indian Institute of Management Indore（印度管理学院印多尔分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对两时期估计经济网络中的换手率受图选择误差影响的问题，提出在齐性边误分类模型下基于观察换手率和图大小的闭式无偏调整方法，并分析稀疏性、校准误差及跨期依赖的影响，给出一致性条件。
AI 中文摘要
经济网络通常分别在两个时期内进行估计，其边集的变化被解释为结构性重组。由于两个网络都是估计得到的，观察到的换手率也反映了图选择误差。我们在齐性边误分类模型下研究了两快照汉明换手率泛函。在已知敏感性和特异性以及跨时期估计边指标条件独立的情况下，潜在换手率允许基于仅观察到的换手率和两个估计图大小进行闭式无偏调整。然后我们检验了稀疏性、校准误差和跨时期依赖的影响。当真链接数量与 $p$ 成比例时，数量级为 $p^{-1}$ 的假阳性概率会产生数量级为 $p$ 的期望虚假换手率。在校准假阳性概率时相同数量级的误差同样会在调整后留下数量级为 $p$ 的偏差。我们还推导了跨时期依赖引起的偏差，并给出了相对于网络规模一致性的充分条件。数值结果说明了有限样本的含义。
英文摘要
Economic networks are often estimated separately over two periods, and changes in their edge sets are interpreted as structural rewiring. Since both networks are estimated, observed turnover also reflects graph-selection error. We study the two-snapshot Hamming-turnover functional under a homogeneous edge-misclassification model. With known sensitivity and specificity and conditional independence of the estimated edge indicators across periods, latent turnover admits a closed-form unbiased adjustment based only on observed turnover and the two estimated graph sizes. We then examine the effects of sparsity, calibration error and dependence across periods. When the number of true links is proportional to $p$, a false-positive probability of order $p^{-1}$ generates expected spurious turnover of order $p$. An error of the same order in calibrating the false-positive probability can likewise leave order-$p$ bias after adjustment. We also derive the bias induced by cross-period dependence and give sufficient conditions for consistency relative to network size. Numerical results illustrate the finite-sample implications.
