Negative Binomial Model with an Application to Special Treatment Count Data
Special Treatment (ST) is a particular regulation for the exchange to prompt the risk to investors. Since ST count data are discrete, traditional Orthogonal Least Square method cannot get better goodness of fitting. However, Poisson regression is appropriate to be used to analyze discrete data. In addition, once it is over-dispersed, Negative Binomial (NB) regression model is more suitable. In this paper, we study the influence of nonfinancial factors on ST occurrence event count with NB model. Result shows that China Interbank Offered Rate, Import, Export, Deposits of Enterprises and one-period lagged ST count are evidently associated with ST risk.
Zhongxin Ni Ting Li
Department of Finance School of Economics, Shanghai University Shanghai, China
国际会议
International Conference on Management and Service Science(2011年第五届管理与服务科学国际会议 MASS 2011)
武汉
英文
1-4
2011-08-12（万方平台首次上网日期，不代表论文的发表时间）