利用广义估计方程估计空间数据的非线性模型
Using generalized estimating equations to estimate nonlinear models with spatial data
摘要：
本文在拟极大似然估计（QMLE）框架下，研究了截面数据非线性模型的两步广义估计方程（GEE）估计。为了提高效率，我们提出了一个分组估计，以考虑潜在的空间相关性的基础创新。我们使用泊松模型和负二项II模型的计数数据和二进制响应数据的概率模型，以证明GEE程序。在弱相依假设下，给出了估计的相合性和渐近正态性的结果。蒙特卡罗模拟表明，我们的方法在计数数据和二进制响应数据的不同估计方法的比较效率增益。最后，本文运用GEE方法研究了中国外商直接投资（FDI）流入的影响因素。
英文摘要：
In this paper, we study estimation of nonlinear models with cross sectional data using two-step generalized estimating equations (GEE) in the quasi-maximum likelihood estimation (QMLE) framework. In the interest of improving efficiency, we propose a grouping estimator to account for the potential spatial correlation in the underlying innovations. We use a Poisson model and a Negative Binomial II model for count data and a Probit model for binary response data to demonstrate the GEE procedure. Under mild weak dependency assumptions, results on estimation consistency and asymptotic normality are provided. Monte Carlo simulations show efficiency gain of our approach in comparison of different estimation methods for count data and binary response data. Finally we apply the GEE approach to study the determinants of the inflow foreign direct investment (FDI) to China.
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