Economists often rely on estimates of linear fixed effects models produced by other teams of researchers. Assessing the uncertainty in these estimates can be challenging. I propose a form of sample splitting for networks that partitions the data into statistically independent branches, each of which can be used to compute an unbiased estimate of the parameters of interest in two-way fixed effects models. These branches facilitate uncertainty quantification, moment estimation, and shrinkage. Drawing on results from the graph theory literature on tree packing, I develop algorithms to efficiently extract branches from large networks. I illustrate these techniques using a benchmark dataset from Veneto, Italy that has been widely used to study firm wage effects.

翻译：经济学家常常依赖其他研究团队估计的线性固定效应模型。评估这些估计值的不确定性可能具有挑战性。本文提出一种用于网络的样本分割方法，该方法将数据划分为统计上独立的分支，每个分支均可用于计算双向固定效应模型中目标参数的无偏估计。这些分支有助于进行不确定性量化、矩估计以及收缩估计。借鉴图论中关于树填充的研究成果，我开发了从大型网络中高效提取分支的算法。我使用一个来自意大利威尼托地区的基准数据集来演示这些技术，该数据集已被广泛用于研究企业工资效应。