Improved Estimation and Specification Testing with Parametric, Nonparametric and Neural Network Models Using the Bootstrap

Halbert白色SBR-9511253 私家侦探提出了用自助法确定模型规格误差检验的抽样分布。 自举法在统计学和规格检验中迅速增加，其中基于渐近分布的临界值似乎经常导致过度拒绝正确的规格，是应用这种强大方法的自然场所。 将被研究的测试的例子是：信息矩阵测试，基于非参数估计量和神经网络估计量的测试，以及是否属于回归的特定变量子集的测试。 私家侦探还建议开发高故障估计，是不敏感的大量的数据污染。 私家侦探还打算将开发的方法应用到一些经验应用中。

Halbert White SBR-9511253 The P.I. proposes to use the bootstrap to determine the sampling distribution of model specification error tests. The bootstrap has been increasing rapidly in statistics and specification tests, where critical values based on asymptotic distributions appear to often lead to over-rejection of correct specifications, are a natural place to apply this powerful method. Examples of tests that will be studied are: information matrix tests, tests based on nonparametric estimators and neural net estimators, and test of whether a particular subset of variables belong in a regression. The P.I. also proposes to develop high breakdown estimators that are insensitive to significant amounts of data contamination. The P.I. also intends to apply the methods developed to a number of empirical applications.