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

P.I.建议使用自举法来确定模型规格误差测试的抽样分布。自举法在统计和规范测试中迅速增长，其中基于渐近分布的临界值似乎经常导致对正确规范的过度拒绝，这是应用这种强大方法的自然场所。将研究的测试示例有：信息矩阵测试，基于非参数估计器和神经网络估计器的测试，以及变量的特定子集是否属于回归的测试。pi还建议开发对大量数据污染不敏感的高击穿估计器。pi还打算将开发的方法应用于一些经验应用。

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.