Sieve IV estimation of cross-sectional interaction models with nonparametric endogenous effect
提出一种筛分工具变量估计方法，用于处理横截面交互模型中内生效应的非线性非参数形式，并给出线性性检验，实证分析区域经济绩效数据。
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.