Copula-based estimation for binary choice panel models with cross-sectional dependence.

Cross-sectional dependence is often encountered in study. The MLE estimators are inefficient when cross section independence is assumed. Therefore, Copula-based log-likelihood functions are introduced to consider the cross section dependence. To avoid the nuisance parameters problem, a method called two-step MLE is proposed. The consistency and the asymptotic normality of the estimator are proved. And the asymptotic covariance matrix is given as well. Monte Carlo experiments show that the two-step MLE improves the efficiency of the estimators significantly. This model is applied to study the characters that affect the cash dividend policies of the listed companies, and it is shown that the sale growth rate becomes more significant relatively. [ABSTRACT FROM AUTHOR]