We study a ridge estimator for the high-dimensional two-way fixed effect regression model with a sparse bipartite network. We develop concentration inequalities showing that when the ridge parameters increase as the log of the network size, the bias, and the variance-covariance matrix of the vector of estimated fixed effects converge to deterministic equivalents that depend only on the expected network. We provide simulations and an application using administrative data on wages for worker-firm matches.

翻译：本文研究具有稀疏二分网络结构的高维双向固定效应回归模型的岭估计方法。我们建立了集中不等式，证明当岭参数随网络规模的对数增长时，估计固定效应向量的偏差及其方差-协方差矩阵将收敛至仅依赖于期望网络的确定性等价形式。我们通过数值模拟和基于工人-企业匹配工资管理数据的实证应用验证了理论结果。