Tobit model with covariate dependent thresholds
Tobit model with covariate dependent thresholds
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DOI：
10.1016/j.csda.2009.02.005
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发表时间：
2010-11
期刊：
影响因子：
--
通讯作者：
Yasuhiro Omori;Koji Miyawaki
中科院分区：
文献类型：
--
作者：
Yasuhiro Omori;Koji Miyawaki
Tobit模型被扩展到允许依赖于个人特征的阈值。在这样的模型中，参数受到尽可能多的不等式约束的观察的数量，和最大似然估计，需要的数值最大化的可能性往往是难以实现的。使用贝叶斯方法，吉布斯抽样算法提出，并进一步，收敛到后验分布的速度加快，通过引入一个额外的尺度变换步骤。利用模拟数据、工资数据和基本利率变化数据说明了该过程。
Tobit models are extended to allow threshold values which depend on individuals’ characteristics. In such models, the parameters are subject to as many inequality constraints as the number of observations, and the maximum likelihood estimation which requires the numerical maximisation of the likelihood is often difficult to be implemented. Using a Bayesian approach, a Gibbs sampler algorithm is proposed and, further, the convergence to the posterior distribution is accelerated by introducing an additional scale transformation step. The procedure is illustrated using the simulated data, wage data and prime rate changes’ data.