Sieve IV estimation of cross-sectional interaction models with nonparametric endogenous effect
Sieve IV estimation of cross-sectional interaction models with nonparametric endogenous effect
复制标题
具有非参数内生效应的横截面相互作用模型的 Sieve IV 估计
DOI：
10.1016/j.jeconom.2020.11.008
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发表时间：
2021
影响因子：
6.3
通讯作者：
Hoshino Tadao
中科院分区：
文献类型：
--
作者：
Nobuhiro Nakamura;Kazuhiko Ohashi;and Daisuke Yokouchi;Hoshino Tadao
在本研究中，我们考虑横截面的相互作用模型，包括空间自回归模型和同伴效应模型作为特殊情况。我们的模型允许内生效应--他人的结果对自己的结果的影响--是非线性和非参数的。对于模型的估计，我们提出了一个筛分工具变量估计，并建立了它的相合性和渐近正态性。此外，我们提出了一个非参数规格测试的线性的内源性效应。在线性的零假设下，我们证明了检验统计量是渐近正态分布的。作为一个实证说明，我们专注于Gennaioli等人调查的区域经济绩效的数据。（2013年）。这种实证分析突出了所提出的模型和方法的实用性。
In this study, we consider cross-sectional interaction models including spatial autoregressive models and peer effects models as special cases. Our model allows the endogenous effect – the effect of others’ outcomes on one’s own outcome – to be nonlinear and nonparametric. For the model estimation, we propose a sieve instrumental variable estimator and establish both its consistency and asymptotic normality. Furthermore, we propose a nonparametric specification test for the linearity of the endogenous effect. Under the null hypothesis of linearity, we show that the test statistic is asymptotically distributed as normal. As an empirical illustration, we focus on the data on regional economic performance investigated by Gennaioli et al. (2013). This empirical analysis highlights the usefulness of the proposed model and method.