Diffusion over a network refers to the phenomenon of a change of state of a cross-sectional unit in one period leading to a change of state of its neighbors in the network in the next period. One may estimate or test for diffusion by estimating a cross-sectionally aggregated correlation between neighbors over time from data. However, the estimated diffusion can be misleading if the diffusion is confounded by omitted covariates. This paper focuses on the measure of diffusion proposed by He and Song (2022), provides a method of decomposition analysis to measure the role of the covariates on the estimated diffusion, and develops an asymptotic inference procedure for the decomposition analysis in such a situation. This paper also presents results from a Monte Carlo study on the small sample performance of the inference procedure.
翻译:在网络上扩散是指一个跨部门单位在某一时期改变状态,导致其邻国在下一个时期改变网络状态的现象,人们可以通过从数据中估计邻国之间长期的跨部门综合关联来估计或测试扩散情况,然而,如果传播被省略的共变体混杂在一起,估计的传播可能会产生误导,本文侧重于他和宋(2022年)提议的传播程度,提供了分解分析方法,以衡量共变体在估计传播中的作用,并为在这种情况下进行分解分析制定了一种无症状的推论程序,本文件还介绍了蒙特卡洛关于微小样本的推断程序绩效研究的结果。