Bootstrap and Semiparametric Methods in Econometrics

9617925自举是一种通过重新抽样数据来估计估计量或检验统计量的分布的方法。它相当于为了评估利益分配的目的而将数据视为总体。在适当的条件下，Bootstrap对检验统计量的分布提供了比一阶渐近理论更好的有限样本近似和更好的有限样本临界值。因此，使用通过Bootstrap获得的临界值，检验的真实水平和名义水平以及可信区间的超额概率之间的差异通常比基于一阶渐近理论的临界值小得多。Bootstrap已经在许多经济应用中展示了测试水平的有限样本扭曲程度的显著减少。这项拟议的研究开发了将Bootstrap应用于应用计量经济学中的几个重要问题的方法，在这些问题中，Bootstrap可能比替代方法具有实质性的优势，但使用标准Bootstrap理论无法令人满意地处理这一点。它们是：a.基于非光滑估计的假设检验；b.半参数估计的带宽选择；c.基于工具变量的假设检验(Iv)弱工具估计。?？

9617925 Horowitz The bootstrap is a method of estimating the distribution of an estimator or test statistic by resampling one's data. It amounts to treating the data as if they were the population for the purpose of evaluating the distribution of interest. Under suitable conditions the bootstrap provides a better finite-sample approximation to the distribution of a test statistic and better finite-sample critical values than does first-order asymptotic theory. As a result, the differences between the true and nominal levels of tests and overage probabilities of confidence intervals are often much smaller using critical values obtained through the bootstrap than critical values based on first-order asymptotic theory. Bootstrap has demonstrated for a number of economic applications spectacular reductions in finite-sample distortions of the levels of tests. The proposed research develops methods for applying the bootstrap to several important problems in applied econometrics where the bootstrap may have substantial advantages over alternative methods but that cannot be handled satisfactorily using standard bootstrap theory. These are: a. Hypothesis tests based on non-smooth estimators; b. Bandwidth selection for semiparametric estimators; c. Hypothesis tests based on instrumental-variables (IV) estimation with weak instruments. ??