Semiparametric Estimation Using Pairwise Comparisons and Other 'Trimming' Methods

该项目的贡献来自于 模型的半参数估计，以前没有 考虑，以及新的估计模型，已被 在统计和计量经济学文献中考虑。 一个伟大 计量经济学的大量研究一直致力于 半参数方法的发展，因为这些方法允许 分析师从数据中获得结果，即使知道的很少 产生数据的基本过程。 这个项目开发了一些半参数估计， 不同的型号。 这些模型包括部分线性Tobit模型， 部分线性logit模型，部分线性Poisson回归 模型，部分线性持续时间模型，以及Tobit模型， 样本选择 这些估计量通过最小化 目标函数是基于比较所有对 意见。 的渐近性质的研究 以这种方式定义的估计器将被执行。 以前对固定效应Tobit模型的研究将继续下去。 估计固定效应版本的可能性，所以- 一种称为类型3的Tobit模型将得到解决， 估计器开发。 将开发软件， 其他人使用这些技术。

The contribution of this project comes from the development of semiparametric estimators for models that have not previously been considered, as well as new estimators for models that have been considered in the statistical and econometric literature. A great deal of research in econometrics has been devoted to the development of semiparametric methods because these methods permit analysts to obtain results from data even if very little is known about the underlying processes that generated the data. This project develops semi-parametric estimators for a number of different models. These include partially linear Tobit models, partially linear logit models, partially linear Poisson regression models, partially linear duration models, and Tobit models with sample selection. These estimators are defined by minimizing objective functions that are based on comparing all pairs of observations. An investigation of the asymptotic properties of estimators that are defined in this way will be conducted. Previous research on Tobit models with fixed effects will continue. The possibility of estimating fixed effects versions of the so- called Type 3 Tobit model will be addressed, and a two-step estimator is developed. Software will be developed to permit others to use these techniques.