论文标题
估计具有固定效果的非线性网络数据模型
Estimating Nonlinear Network Data Models with Fixed Effects
论文作者
论文摘要
我引入了一种新方法，以校正具有特定固定效应的二元模型，包括具有同质和程度异质性的二元链接形成模型。所提出的方法使用折刀程序来处理偶然参数问题。该方法可以应用于有向和无向网络，允许进行非二进制结果变量，并可用于偏向对平均效应和反事实结果的正确估计。我还展示了如何使用折刀来偏向于依赖多个节点的函数的固定效应平均值，例如网络中的三合会或四核。例如，我实施了跨二元组依赖性的规范测试，例如互惠或传递性。最后，我证明了估算器在重力模型应用程序中的实用性，用于跨国家的进出口关系。
I introduce a new method for bias correction of dyadic models with agent-specific fixed effects, including the dyadic link formation model with homophily and degree heterogeneity. The proposed approach uses a jackknife procedure to deal with the incidental parameters problem. The method can be applied to both directed and undirected networks, allows for non-binary outcome variables, and can be used to bias correct estimates of average effects and counterfactual outcomes. I also show how the jackknife can be used to bias correct fixed-effect averages over functions that depend on multiple nodes, e.g. triads or tetrads in the network. As an example, I implement specification tests for dependence across dyads, such as reciprocity or transitivity. Finally, I demonstrate the usefulness of the estimator in an application to a gravity model for import/export relationships across countries.
