中文摘要：

基于双变量FGM类函数的Copula方法可以简单有效估计带截面相关的固定效应二元面板数据模型.为了能够构造出Copula分布函数,我们在经典的条件Logit的估计之后再做一次极大似然估计,得到异质性的估计,这样有了参数和异质性就能得到残差从而估计得到相关系数,进而构造Copula分布函数.蒙特卡罗模拟发现基于Copula得到的参数估计量在收敛速度上要明显好于经典的条件Logit估计量.

英文摘要：

This paper proposes a method to estimate binary-choice panel models with fixed effect and cross-sectional dependence based on FGM family bivariates copula function. To construct copula-based likelihood functions, we estimate a common intercept in place of the heterogeneities after estimating the interested parameters with conditional Logit models. With these results, we can compute the general residuals and the correlation coefficients, and thus construct the copula- based likelihood functions. Monte Carlo simulation shows copula-based estimators have better convergence rate than classical conditional Logit estimators.