Semiparametric and Nonparametric Estimation off Structural Models

该项目将开发新的方法来估计 半参数非线性计量经济模型参数 或非参数方法。在三个主要方面开展工作， 将进行研究。 首先，线性模型的参数估计服从 样本选择将在弱非参数下考虑 对选择修正形式的限制。 具体地说，选择效应将被假定为仅取决于 在一些可观察的选择变量的条件平均值上， 其将使用非参数回归方法来估计; 此估计值将出现在第二步估计值中， 感兴趣的参数。 第二，结构方程的非参数估计， 将研究仪器变量。这将涉及 积分方程解的非参数估计 加性回归模型的非参数估计 对回归变量进行初步非参数估计。 第三，估计量可达效率的界 单调回归模型将得出时，误差 分布满足单个条件分位数限制。 此外，一个可行的估计程序，达到这个界限， 将被开发。 这一研究方向将继续推动 计量经济学中的半参数和非参数估计。 这些方法将非常有益于发展 经济模型，因为允许更灵活的假设， 为传统估算技术提供基准。

The project will develop new methods for estimating the parameters of nonlinear econometric models using semiparametric or nonparametric approaches. Work on three broad lines of research will be undertaken. First, estimation of parameters of linear models subject to sample selection will be considered under a weak nonparametric restriction on the form of the selection correction. Specifically, the selection effect will be assumed to depend only on the conditional mean of some observable selection variable, which is to be estimated using a nonparametric regression method; this estimate will appear in the second-step estimator for the parameters of interest. Second, nonparametric estimation of structural equations using instrumental variables will be investigated. This will involve nonparametric estimation of solutions of integral equations, as well as nonparametric estimation of additive regression models with preliminary nonparametric estimates of regressors. Third, a bound for the attainable efficiency of estimators for monotonic regression models will be derived when the error distribution satisfies a single conditional quantile restriction. Also, a feasible estimation procedure which attains this bound will be developed. This line of research will continue to push the frontier of semiparametric and nonparametric estimation in econometrics. These methods will be extremely beneficial in the development of economic modelling, as more flexible assumptions are allowed and provide benchmarks for traditional estimation techniques.