Nonparametric and Semiparametric Econometrics Using Artifical Neural Networks

利用最近发展起来的计量经济学和数学方法， 统计学，本项目将调查的理论， "学习"一类"人工神经网络"模型。 将要开发的模型可能会产生更多的 高效和强大的评估和测试程序。 研究福尔斯分为四个相互关联的方面：大样本 基于神经网络的非参数估计程序的性质 网络模型，不同的相对计算特性 优化网络学习（估计）的方法 目标函数，非参数网络应用 程序来构建半参数估计和执行 规格测试和基于网络的程序的比较 标准的非参数和半参数程序， 核估计和系列估计等。 在这一领域开发的技术已经成功地 用于工程技术中的预测。一些具体 应用包括各种工业环境， 防御完成任务，一直无法解决。一些 例子包括机场的炸弹探测，区分 岩石和地雷产生的声纳信号， 机械、手写字符识别和机器人中的阻尼 协调和控制。这方面的进一步工作应 继续扩大经济学以外的应用领域。

Using methods recently developed in econometrics and mathematical statistics, this project will investigate the theory of "learning" in a class of "artificial neural network" models. Models are to be developed that will yield potentially more efficient and powerful estimation and testing procedures. The research falls into four interrelated ares: large-sample properties of nonparametric estimation procedures based on neural network models, relative computational properties of different methods for optimizing the network learning (estimation) objective function, application of nonparametric network-based procedures to construct semi-parametric estimators and perform specification tests, and comparison of network-based procedures to standard nonparametric and semi-parametric procedures based on kernel estimators and series estimators, among others. The techniques developed in this field have been successfully used to forecast in engineering and technology. Some specific applications have included various industrial settings and in defense to accomplish tasks that had been insoluble. Some examples include bomb detection in airports, distinguishing between sonar signals generated from rocks and mines, vibration damping in machinery, handwriting character recognition and robot coordination and control. Further work in this area should continue to expand the fields of applications beyond economics.