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摘要翻译：
本文讨论了由于观测通过观测网络相互连接而引起的横截面依赖性。继Doukhan和Louhichi(1999)之后，我们用非线性变换变量的协方差来度量依赖强度。我们给出了网络因变量的大数定律和中心极限定理。我们还提供了一种计算标准误差的方法，该方法对一般形式的网络依赖具有鲁棒性。为此，我们依赖于一个网络异方差和自相关一致性(HAC)方差估计器，并证明了它的一致性。这些结果依赖于一种条件，这种条件的特征是在网络中依赖关系的衰减率和网络的密集度之间进行权衡。我们的方法可以适应由网络形成模型、图上的随机场、条件依赖图和大型函数-因果方程组生成的数据。
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英文标题：
《Limit Theorems for Network Dependent Random Variables》
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作者：
Denis Kojevnikov, Vadim Marmer, Kyungchul Song
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最新提交年份：
2021
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
This paper is concerned with cross-sectional dependence arising because observations are interconnected through an observed network. Following Doukhan and Louhichi (1999), we measure the strength of dependence by covariances of nonlinearly transformed variables. We provide a law of large numbers and central limit theorem for network dependent variables. We also provide a method of calculating standard errors robust to general forms of network dependence. For that purpose, we rely on a network heteroskedasticity and autocorrelation consistent (HAC) variance estimator, and show its consistency. The results rely on conditions characterized by tradeoffs between the rate of decay of dependence across a network and network's denseness. Our approach can accommodate data generated by network formation models, random fields on graphs, conditional dependency graphs, and large functional-causal systems of equations.
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PDF链接：
https://arxiv.org/pdf/1903.01059
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