Sieve IV estimation of cross-sectional interaction models with nonparametric endogenous effect

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.