Using generalized estimating equations to estimate nonlinear models with spatial data
研究了在准极大似然估计框架下，用两步广义估计方程估计截面非线性模型，提出分组估计量处理空间相关，蒙特卡洛模拟显示效率提升，并用中国贸易数据验证文化距离对贸易的负向影响。
.We study the estimation of nonlinear models with cross-sectional data using two-step generalized estimating equations within the quasi-maximum likelihood estimation framework. To improve efficiency, we propose a grouped estimator that accounts for potential spatial correlation in the underlying innovations of nonlinear models. Under mild weak dependence assumptions, we provide results on estimation consistency and asymptotic normality. Monte Carlo simulations demonstrate the efficiency gain of our approach compared to various estimation methods. Finally, we apply the proposed approach to examine the role of cultural distance in an extended gravity equation using international trade data from China. Compared to existing methods, our approach yields estimates with smaller standard errors and reinforces the hypothesis that both cultural and geographical distances significantly negatively influence international trade.