Nonlinear Spatial Dynamic Panel Data Models with Endogenous Dominant Units: An Application to Share Data
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含内生主导单元的非线性空间动态面板模型：份额数据应用
- 研究问题
- 如何在非线性空间动态面板中同时处理主导单元、双向固定效应、异方差及内生空间权重，并估计份额数据的互动效应？
- 方法与思路
- 作者允许权重矩阵列和无界，在近纪元依赖框架下研究准最大似然、GMM 和求根估计，并推导边际效应及极限分布。
- 材料陈述的贡献
- 摘要称，三类估计量具有一致性和渐近正态性，求根估计较易计算；中国城市应用发现第三产业份额存在显著溢出。
- 阅读关注点
- 应核对权重内生性的处理方式、主导单元强度及双维渐近条件，以及非线性边际效应如何区分直接与跨城市影响。
- 阅读边界
- 仅依据所给 OpenAlex 版本摘要；未列出完整识别与依赖条件、效应数值，所述城市溢出不能仅凭摘要确认为因果关系。
原文摘要
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.