Nonlinear Spatial Dynamic Panel Data Models with Endogenous Dominant Units: An Application to Share Data
提出了一个非线性空间动态面板数据模型，允许空间权重矩阵存在无界列和以处理主导单元效应，并考虑社会经济距离构建的空间权重矩阵的内生性。基于近邻相依框架，研究了三种估计量的一致性和渐近正态性，并应用于中国地级市数据，发现第三产业份额存在显著的空间溢出效应。
This article develops a nonlinear spatial dynamic panel data model with one particularly interesting application to a structural interaction model for share data. To account for effects from dominant (popular) units, the spatial weights matrix in our model can allow for unbounded column sums. To account for heterogeneity, our model includes two-way fixed effects and heteroscedastic errors. We further consider the potential endogeneity of the spatial weight matrix constructed from socioeconomic distance. We investigate the quasi-maximum likelihood estimator (QMLE), generalized methods of moments estimator (GMME), and root estimator (RTE), and establish their consistency and asymptotic normality based on the near epoch dependence (NED) framework. The RTE can derive a relatively computationally simple and closed-form solution without evaluating the QMLE’s Jacobian matrix as well as the iterations by GMME. We consider both n, T→∞ , and the strength of the dominant units is equal to 1 when T→∞ . For the purpose of empirical analysis, we derive the marginal effects and their limiting distributions based on the proposed estimators. In an empirical application, we apply our model to China’s prefecture city-level data, revealing significant spillover effects of the tertiary industry share. These findings suggest that the development of the tertiary sector in large cities can foster its growth in small cities.