一种基于Tobit回归模型的序贯压缩估计方法研究

A Sequential Shrinkage Estimate Based on Tobit Regression Model

摘要: Tobit回归模型在计量经济学等研究领域中有着广泛的应用。但是我们在处理面板数据以及时间序列数据时经常会遇到包含太多变量的数据集，而这些变量中只有少数变量对模型有贡献。为了去除这些“无效变量”的影响，在本文中，我们提出一种基于自适应压缩估计的序贯抽样策略来构造“有效”参数的固定长度的置信集，并在自适应设计下对所提出的序贯抽样策略进行数值模拟，最后数值模拟达到了预期的效果。

Abstract: In the applications of Tobit regression models we always encounter the data sets which contain too many variables, but only a few of them contribute to the model. Therefore, it will waste much more samples to estimate the “non-effective” variables in the inference. In this paper, we use a sequential procedure for constructing the fixed size confidence set for the “effective” parameters to the model based on an adaptive shrinkage estimate such that the “effective” coefficients can be efficiently identified with the minimum sample size. Adaptive design is considered for numerical simulation.