Two Stage Least Absolute Deviations Estimators
两阶段最小绝对离差估计量AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要以联立方程模型的结构方程参数估计为对象，并提出两阶段绝对离差估计量，直接对应结构估计方向。
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两阶段最小绝对离差估计量
- 研究问题
- 如何将最小绝对离差法用于联立方程模型中的结构参数估计，并分析两阶段估计量类的渐近性质、选择其中的最优成员？
- 方法与思路
- 作者定义两阶段最小绝对离差估计量类，说明其与两阶段最小二乘的对应关系，推导渐近性质并讨论类内最优选择。
- 材料陈述的贡献
- 摘要称适当定义的两阶段最小绝对离差估计可体现类似 LAD 相对最小二乘的优势，并指出文献中已有的一种构造并不适当。
- 阅读关注点
- 核对两阶段程序的精确定义、识别与误差分布条件、渐近协方差和最优准则，尤其检查此前 LAD 类比构造为何不适当。
- 阅读边界
- 摘要未说明工具变量、识别与分布条件，也未给出效率比较的具体标准；作者所称优势不能推广为对所有模型都优于 2SLS。
原文摘要
In this paper the method of least absolute deviations is applied to the estimation of the parameters of a structural equation in the simultaneous equations model. A class of estimators called two stage least absolute deviations estimators is defined, their asymptotic properties are derived, and the problem of finding the optimal member of the class is considered. IN THIS PAPER WE APPLY the method of least absolute deviations to the estimation of the parameters of a structural equation in the simultaneous equations model. We define a class of estimators called two stage least absolute deviations estimators (2SLAD) and derive their asymptotic properties. They are so named as their relationship to the two stage least squares estimator (2SLS) is analogous to the relationship of the least absolute deviations estimator (LAD) to the least squares estimator (LS) in the standard regression model. The LAD estimation has been extensively studied in the context of the standard regression model and its usefulness is universally recognized. In this paper we show that the advantage of 2SLAD over 2SLS in the simultaneous equations model can be as great as that of LAD over LS in the standard regression model, if 2SLAD is properly defined. This last clause is very important, since the results of this paper indicate that the LAD analogue of 2SLS that has been considered before in the literature is not an appropriate method.