在密集测量误差下估计网络溢出效应
Estimating Network Spillovers under Dense Measurement Error
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中文总结 AI 辅助
研究在邻接矩阵受测量误差污染时网络模型的溢出效应，提出含正则化框架的两种估计器，包括两阶段程序和广义矩估计器，能降低溢出估计均方根误差，应用框架研究经济增长溢出等问题，改进估计结果。
中文摘要 AI 辅助
本文分析了空间（网络）模型中的溢出效应，当邻接矩阵因报告、汇总或披露缺陷导致的测量误差而受到污染时，会导致网络效应估计不一致。我们为潜在网络引入了一个正则化框架，该框架允许稀疏和/或低秩结构，并考虑测量误差与结果之间的潜在相关性。我们提出了两种估计器：一是两阶段程序，先对邻接矩阵去噪，再将净化后的网络纳入回归分析；二是广义矩估计器，联合估计回归参数并优化网络结构。相对于忽略测量误差的朴素估计，我们建立了溢出效应估计器严格改进的一致性速率。模拟表明，在存在噪声网络的情况下，我们的方法相对于传统方法将溢出估计的均方根误差降低了约50%-80%。我们应用该框架研究经济增长的国际溢出效应以及美国各州之间的税收竞争，表明去噪可能恢复列昂惕夫稳定性并产生改进的溢出效应估计。
英文摘要
This paper analyzes spillover effects in spatial (network) models when the neighborhood (adjacency) matrix is contaminated by measurement error from reporting, aggregation, or disclosure imperfections, leading to inconsistent estimation of network effects. We introduce a regularization framework for the latent network that allows for sparse and/or low-rank structure and accommodates potential correlation between measurement errors and outcomes. We propose two estimators: (i) a two-stage procedure that first denoises the adjacency matrix and then incorporates the purified network into a regression analysis, and (ii) a Generalized Method of Moments (GMM) estimator that jointly estimates regression parameters and refines the network structure. We then establish strictly improved consistency rates for the spillover effect estimator relative to naive estimation ignoring measurement error. Simulations demonstrate that, in the presence of noisy networks, our approach reduces the root mean squared error of spillover estimates relative to conventional methods by approximately $50-80\%$. We apply our framework to examine the international spillover of economic growth, and the tax competition across U.S. states, illustrating that denoising might restore Leontief stability and yields improved estimates of spillovers.
