Flexible Bivariate Count Data Regression Models
提出一种半参数估计方法处理双变量计数数据回归，允许正负相关，并扩展至零膨胀、删失数据和多元情形，蒙特卡洛实验和烟草使用实证表明模型优于现有方法。
The article develops a semiparametric estimation method for the bivariate count data regression model. We develop a series expansion approach in which dependence between count variables is introduced by means of stochastically related unobserved heterogeneity components, and in which, unlike existing commonly used models, positive as well as negative correlations are allowed. Extensions that accommodate excess zeros, censored data, and multivariate generalizations are also given. Monte Carlo experiments and an empirical application to tobacco use confirms that the model performs well relative to existing bivariate models, in terms of various statistical criteria and in capturing the range of correlation among dependent variables. This article has supplementary materials online.