论文标题
Heckman Selection-T模型：通过EM-Algorithm进行参数估计
Heckman selection-t model: parameter estimation via the EM-algorithm
论文作者
论文摘要
Heckman选择模型可能是通过样本选择分析数据中最流行的计量经济学模型。该模型的分析基于误差项的正态性假设，但是，在某些应用中，误差项的分布显着偏离正态性，例如，在存在沉重的尾巴和/或非典型观察的情况下。在本文中，我们探讨了Heckman Selection-T模型，其中随机错误遵循双变量学生的T分布。我们开发了一种可以迭代计算参数的最大似然估计值的可分析和有效的EM-TYPE算法，标准误差是副产品。该算法在e-step上具有封闭形式的表达式，该算法依靠公式来实现截短的学生-T分布的平均值和差异。仿真研究表明，Heckman选择 - 正态模型的脆弱性以及Heckman Selection-T模型的鲁棒性方面。分析了两个真实的例子，说明了提出的方法的有用性。提出的算法和方法在新的R软件包Heckmanem中实现。
Heckman selection model is perhaps the most popular econometric model in the analysis of data with sample selection. The analyses of this model are based on the normality assumption for the error terms, however, in some applications, the distribution of the error term departs significantly from normality, for instance, in the presence of heavy tails and/or atypical observation. In this paper, we explore the Heckman selection-t model where the random errors follow a bivariate Student's-t distribution. We develop an analytically tractable and efficient EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters, with standard errors as a by-product. The algorithm has closed-form expressions at the E-step, that rely on formulas for the mean and variance of the truncated Student's-t distributions. Simulations studies show the vulnerability of the Heckman selection-normal model, as well as the robustness aspects of the Heckman selection-t model. Two real examples are analyzed, illustrating the usefulness of the proposed methods. The proposed algorithms and methods are implemented in the new R package HeckmanEM.
