Semiparametric Limited Dependent Variable Estimators, with Applications

提出的研究是构造和经验应用新的半参数和非参数估计有限因变量模型，特别是二元选择，截尾和截断回归模型。该项目在具有重要政策意义的经验应用中采用了新的估计器。付费意愿模型的设计目的是估计人们为公共产品付费意愿的条件分布的均值、中位数和其他特征。删减回归估计器将用于评估税收和收入政策对耐用品和其他消费类别需求的影响，这些类别中有许多“零”。二元选择估计器将应用于女性劳动力供给决策的模型，允许测量不良的协变量，如家庭资产收入。更具体地说，该项目开发了一个根n一致的渐近正态二元选择模型估计器，它允许一些一般形式的未知异方差，以及对有序选择、多项选择和审查回归模型的扩展。二元选择估计器特别适合研究在评估公共产品时出现的“支付意愿”模型。研究者继续他在这类应用程序上的工作，以及使用数据排序方法来消除对第一阶段核密度估计器的需求的估计器的变体。对该估计器进行了面板数据扩展。这种扩展允许系数估计在一个二元选择面板数据模型与固定效应，弱外生回归，和未知的误差分布。估计器的扩展，以处理内源性或误测回归量也将被研究。这些和面板数据扩展将应用于劳动力供给决策的实证研究。在此基础上，提出了截尾和截尾回归模型的一组新的非参数估计量。在这些模型中，对拉tent回归函数和误差分布进行了非参数识别和估计。

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.