Semiparametric Limited Dependent Variable Estimators, with Applications

本论文的主要目的是构造和实证应用新的半参数和非参数估计，用于有限因变量模型，特别是二元选择、删失和截断回归模型。该项目在具有重要政策含义的经验应用中采用了新的估计量。支付意愿模型的目的是估计手段，中位数，和人口的意愿支付公共产品的条件分布的其他功能。审查回归估计将用于评估税收和收入政策对耐用品和其他消费类别的需求的影响，其中观察到许多“零”。二进制选择估计将被应用于模拟女性劳动力供给decisions，考虑到测量不佳的协变量，如家庭资产income.More具体而言，该项目开发了一个根n一致，渐近正态的二进制选择模型估计，允许一些一般形式的未知异方差，沿着扩展到有序选择，多项式选择，和审查回归模型。 二元选择估计量特别适合于研究公共产品估值中出现的“支付意愿”模型。研究者继续他的工作在这类应用中，沿着的变量的估计，使用数据排序方法，以消除需要的第一阶段核密度估计。面板数据扩展的估计也被开发。这种扩展允许在固定效应、弱外生回归量和未知误差分布的二元选择面板数据模型中估计系数。 扩展的估计处理内源性或误测回归也将进行调查。这些和面板数据的扩展将被应用到劳动力供给决策的实证研究。 除此之外，本文还提出了截尾回归模型的一组新的非参数估计。在这些模型中，潜回归函数和误差分布都是非参数识别和估计的。

The proposed research is to construct and empirically apply new semiparametric and nonparametric estimators for limited dependent variable models, especially binary choice, censored, and truncated regression models. The project employs the new estimators in empirical appli-cations having important policy implications. The willingness-to pay models are designed to estimate means, medians, and other features of the conditional distribution of a population's willingness to pay for public goods. The censored regression estimators will be used to assess the effects of tax and income policies on the demand for durables and other consumption categories in which many "zeros" are observed. The binary choice estimator will be applied to model female labor supply decisions, allowing for badly measured covariates such as household asset income.More specifically, the project develops a root n consistent, asymptotically normal binary choice model estimator that permits some general forms of unknown heteroscedasticity, along with extensions to ordered choice, multinomial choice, and censored re-gression models. The binary choice estimator is particularly well suited for studying the "Willingness-To-Pay" models that arise in valuing public goods. The investigator continues his work on this class of applications, along with variants of the estimator that use a data sorting method to eliminate the need for a first stage kernel density estimator.A panel data extension of the estimator is also developed. This extension permits estimation of coefficients in a binary choice panel data model with fixed effects, weakly exogenous regressors, and unknown error distribution. Extensions of the estimator to handle endogenous or mismeasured regres-sors will also be investigated. These and the panel data extension will be applied to an empirical study of labor supply decisions. Separate from the above, a new set of nonparametric estimators for censored and truncated regression models are proposed. In these models, both the la-tent regression function and the distribution of the errors are nonparametrically identified and estimated.