This paper develops an estimation methodology for network data generated from a system of simultaneous equations, which allows for network interdependencies via spatial lags in the endogenous and exogenous variables, as well as in the disturbances. By allowing for higher-order spatial lags, our specification provides important flexibility in modeling network interactions. The estimation methodology builds, among others, on the two-step generalized method of moments estimation approach introduced in Kelejian and Prucha (1998, Journal of Real Estate Finance and Economics 17, 99–121; 1999, International Economic Review 40, 509–533; 2004, Journal of Econometrics 118, 27–50). The paper considers limited and full information estimators, and one- and two-step estimators, and establishes their asymptotic properties. In contrast to some of the earlier two-step estimation literature, our asymptotic results facilitate joint tests for the absence of all forms of network spillovers.

本文开发了一种由联立方程组生成的网络数据的估计方法，该方法允许通过内生变量和外生变量以及干扰的空间滞后来实现网络相互依赖性。通过允许高阶空间滞后，我们的规范为网络交互建模提供了重要的灵活性。除其他外，估计方法建立在 Kelejian 和 Prucha 引入的两步广义矩估计方法的基础上（1998 年，《房地产金融与经济杂志》 17, 99–121；1999 年，《国际经济评论》 40, 509–533 ；2004 年，计量经济学杂志118, 27–50）。本文考虑了有限信息估计器和完全信息估计器以及一步和两步估计器，并建立了它们的渐近性质。与一些早期的两步估计文献相比，我们的渐近结果有助于联合测试不存在所有形式的网络溢出。