Semiparametric Estimation of Multivariate Latent Variable Models

本研究的主要目的是调查一个 一类统计模型估计新方法 广泛应用于经济学和其他社会科学。一般模型 与工作有关的行业是一个依赖 变量不是连续的。 这些模型包括那些数据 删失或聚合导致因变量为 潜在的但未被观察到的离散的分组或排序 连续变量 另一个这样的模型是所谓的离散 选择模型，用于分析许多经济决策，如 作为住房保有权的选择、交通方式和购买 耐用消费品在这些模型中，观察到的数据通常包括 仅仅是观察是否做出了特定的选择， 没有 所有这些类型的模型基本上都是基于 一个潜在的连续变量，通常被称为潜在的 变量，是决定一个人 这是一个特定的决定，但这个变量本身并没有被观察到。 什么 是一个指数或指标，它仅仅显示了选择 经济行为人所做的。 在进行这种分析时， 关于基本变量的假设，特别是关于 它的分布形式，过去的工作表明，研究结果， 很大程度上取决于分布假设。 教授 鲍威尔是计量经济学研究领域的领导者， 对分布假设稳健的估计技术。 从技术上讲，鲍威尔教授在这个项目中推导出半参数 用于分析潜在变量模型的估计器。 的程序 包括两个步骤。 首先，基于一组 模型的外生变量。 对于每个观察点， 潜在变量的条件分布是居中的 在这个指数上。 第二步使用新创建的 指数来估计参数，其中研究人员是 兴趣

The primary object of this research is the investigation of an innovative technique for estimating a class of statistical models widely used in economics and other social sciences. The general model with which the line of work is concerned is one where the dependent variable is not continuous. Such models include those in which data censoring or aggregation results in the dependent variable being a discrete grouping or ordering of an underlying but unobserved continuous variable. Another such model is the so-called discrete choice model, which is used to analyze many economic decisions, such as housing tenure choice, mode of transportation, and the purchase of consumer durables. In these models the observed data usually consists simply of an observation as to whether a particular choice was made or not. All these types of models are fundamentally based on the idea that an underlying continuous variable, often called a latent variable, is really the crucial factor in the person's making a particular decision, but that variable is not itself observed. What is observed is an index or indicator which merely shows the choice made by the economic agent. In doing such analysis one must make assumptions about the underlying variable, and in particular about its distributional form, and past work has shown research results to be quite dependent on the distributional assumptions. Professor Powell is a leader in a line of econometric research which develops estimation techniques robust to distributional assumptions. Technically, Professor Powell in this project derives semiparametric estimators for analyzing latent variable models. The procedure consists of two steps. First, an index is derived based on a group of variables exogenous to the model. Then for each observation in the data the conditional distribution of the latent variable is centered on this index. The second step uses the newly created data set of indices to estimate the parameters in which the researcher is interested.