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摘要翻译：
网络效应的识别是基于群体规模变化、网络结构或在网络中的相对位置。给出了基于邻接矩阵不同特征值个数的无向网络模型辨识的易于验证的必要条件。识别网络效应是可能的；虽然在许多经验性情况下，现有的识别战略可能需要使用许多工具或可能彼此密切相关的工具。使用高度相关的仪器或多个仪器可能导致识别薄弱或多个仪器偏见。本文提出了两阶段最小二乘(2SLS)估计的正则化形式作为这些问题的解决方案。所提出的估计量是相合的和渐近正态的。蒙特卡罗研究说明了正则估计的性质。通过对一个地方政府税收竞争模型的实证分析，验证了正则化方法的实证相关性。
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英文标题：
《Weak Identification and Estimation of Social Interaction Models》
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作者：
Guy Tchuente
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最新提交年份：
2019
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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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英文摘要：
The identification of the network effect is based on either group size variation, the structure of the network or the relative position in the network. I provide easy-to-verify necessary conditions for identification of undirected network models based on the number of distinct eigenvalues of the adjacency matrix. Identification of network effects is possible; although in many empirical situations existing identification strategies may require the use of many instruments or instruments that could be strongly correlated with each other. The use of highly correlated instruments or many instruments may lead to weak identification or many instruments bias. This paper proposes regularized versions of the two-stage least squares (2SLS) estimators as a solution to these problems. The proposed estimators are consistent and asymptotically normal. A Monte Carlo study illustrates the properties of the regularized estimators. An empirical application, assessing a local government tax competition model, shows the empirical relevance of using regularization methods.
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PDF链接：
https://arxiv.org/pdf/1902.06143
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