A revised Tobit procedure for mitigating bias in the presence of non‐zero censoring with an application to milk‐market participation in the Ethiopian highlands
针对传统Tobit模型假设截断点为零导致参数估计偏差的问题，提出基于贝叶斯方法的修正技术，并通过埃塞俄比亚高地牛奶市场参与数据验证其有效性。
Abstract Fixed transactions costs that prohibit exchange engender bias in supply analysis due to censoring of the sample observations. The associated bias in conventional regression procedures applied to censored data and the construction of robust methods for mitigating bias have been preoccupations of applied economists since Tobin [Econometrica 26 (1958) 24]. This literature assumes that the true point of censoring in the data is zero and, when this is not the case, imparts a bias to parameter estimates of the censored regression model. We conjecture that this bias can be significant; affirm this from experiments; and suggest techniques for mitigating this bias using Bayesian procedures. The bias‐mitigating procedures are based on modifications of the key step that facilitates Bayesian estimation of the censored regression model; are easy to implement; work well in both small and large samples; and lead to significantly improved inference in the censored regression model. These findings are important in light of the widespread use of the zero‐censored Tobit regression and we investigate their consequences using data on milk‐market participation in the Ethiopian highlands.