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摘要翻译：
本文分析了一个存在未观察到的Agent特定异质性的网络形成的半参数模型。其目的是在未观测因素的分布不是参数指定的情况下，识别和估计与观测属性同源相关的偏好参数。本文对网络形成的文献有两个主要贡献。首先，建立了一个新的参数向量点辨识结果，该结果依赖于一个特殊阻遏器的存在。该辨识证明是构造性的，并刻画了感兴趣参数的闭式。其次，给出了参数向量的一个简单的两步半参数估计，并给出了一个第一步核估计。该估计器计算简单，可用于稠密网络和稀疏网络。此外，我证明了估计量是一致的，并且随着网络中个体数量的增加，估计量具有一个极限正态分布。Monte Carlo实验表明，该估计器在有限样本和不同稀疏程度的网络中具有良好的性能。
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英文标题：
《A Semiparametric Network Formation Model with Unobserved Linear
Heterogeneity》
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作者：
Luis E. Candelaria
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最新提交年份：
2020
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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：
This paper analyzes a semiparametric model of network formation in the presence of unobserved agent-specific heterogeneity. The objective is to identify and estimate the preference parameters associated with homophily on observed attributes when the distributions of the unobserved factors are not parametrically specified. This paper offers two main contributions to the literature on network formation. First, it establishes a new point identification result for the vector of parameters that relies on the existence of a special repressor. The identification proof is constructive and characterizes a closed-form for the parameter of interest. Second, it introduces a simple two-step semiparametric estimator for the vector of parameters with a first-step kernel estimator. The estimator is computationally tractable and can be applied to both dense and sparse networks. Moreover, I show that the estimator is consistent and has a limiting normal distribution as the number of individuals in the network increases. Monte Carlo experiments demonstrate that the estimator performs well in finite samples and in networks with different levels of sparsity.
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PDF链接：
https://arxiv.org/pdf/2007.05403
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