Non Parametric Estimationa and Testing with Demand Applications

最近有大量的理论计量经济学 工作在半参数和非参数估计，收敛 等于或接近参数根N率。 这些估计一般 包括估计有限数量的参数， 无限维滋扰参数的存在（ 非参数分量）。 目标相当精确 估计和测试感兴趣的参数，同时最小化 或者消除任意函数形式限制。 这个项目 对计量经济学理论的贡献 特别是与实证工作相关的。 该项目应用了 消费者需求中的一系列问题 分析. 更具体地说，该项目有四个密切相关的部分： 1)改进和扩展现有的技术估计有限 未知函数及其导数的维数期望 根施氮量; 2)构建基于矩的一致非参数假设 回归、密度或导数约束的测试; 3)构造一个半参数的，“不搜索”的，根N收敛 可处理离散的多线性指数模型的估计量 以及连续回归，并能处理一般形式 包括随机系数的异方差; 4)将上述应用于Slutsky的非参数估计 替代矩阵，同质性的非参数检验， Slutsky对称性，离散或多重半参数估计 有限因变量选择模型和其他横截面模型 需求应用。

There has recently been a great deal of theoretical econometric work in semiparametric and nonparametric estimates that converge at or near parametric, root N rates. These estimators generally consist of estimating as finite number of parameters in the presence of an infinite dimensional nuisance parameter (the nonparametric component). The goal is reasonably precise estimation and testing of parameters of interest while minimizing or eliminating arbitrary function form restrictions. This project contributes to this body of econometric theory in ways that are particularly relevant for empirical work. The project applies the theory empirically to a range of problems in consumer demand analysis. More specifically, the project has four closely interrelated parts: 1) Refining and extending existing techniques of estimating finite dimensional expectations of unknown functions and their derivatives at root N rates; 2) Constructing moment based consistent, nonparametric hypothesis tests of regression, density, or derivative constraints; 3) Constructing a semiparametric, "no search," root N Converging estimator of multiple linear index models that can handle discrete as well as continuous regressors, and can deal with general forms of heteroscedasticity including random coefficients; 4) Applying the above to nonparametric estimation of Slutsky substitution matrices, nonparametric testing of homogeneity and Slutsky symmetry, semiparametric estimation of discrete or multiple limited dependent variable choice models, and other cross sectional demand applications.