AI 中文总结
研究高维面板网络模型中潜在对偶结构的估计和推断，提出直接从结构模型用观测数据识别估计网络的新方法，经顺序工具变量筛选实现网络恢复，建立相关推断，应用于企业杠杆数据揭示交互作用共存。
AI 中文摘要
我们开发了一种新方法，用于具有潜在对偶结构的高维面板网络模型的估计和推断。该框架允许结果同时受到正向和负向交互通道的影响，适用于一些交互增强结果而另一些产生竞争和替代效应的情况。所提方法使用观测数据直接从结构模型中识别和估计网络，无需预先指定网络。通过顺序工具变量筛选程序实现网络恢复。我们建立了精确的支持恢复和后选择推断。对美国企业杠杆数据的应用揭示了企业财务决策中增强和替代交互作用的共存。
英文摘要
We develop a novel methodology for estimation and inference in high-dimensional panel network models with latent dual structures. The framework allows outcomes to be affected simultaneously by positive and negative interaction channels, accommodating settings in which some interactions reinforce outcomes while others generate competition and displacement effects. The proposed method identifies and estimates the network directly from the structural model using observed data without the need to pre-specify the network. Network recovery is achieved through a sequential instrumental-variable screening procedure. We establish exact support recovery and oracle-equivalent post-selection inference. An application to U.S. corporate leverage data reveals the coexistence of reinforcing and displacement interactions in firms' financial decisions.
