Bootstrap and Semiparametric Methods in Econometrics

计量经济学中的 Bootstrap 和半参数方法

• 批准号: 9617925
• 负责人: Joel Horowitz
• 金额: $ 21.22万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-03-15 至 2001-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9617925&HistoricalAwards=false
• 关键词: Bootstrap; Semiparametric; Methods; Econometrics

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. ??

小行星9617925 Bootstrap是一种通过回归数据估计估计量或检验统计量的分布的方法。 这相当于为了评估利益分布而将数据视为人口。 在适当的条件下，自举提供了一个更好的有限样本近似的分布的检验统计量和更好的有限样本临界值比一阶渐近理论。 因此，真实和标称水平的测试和超额置信区间的概率之间的差异往往是小得多的临界值通过自助获得比临界值的基础上的一阶渐近理论。 Bootstrap在许多经济应用中已经证明了检验水平的有限样本失真的显著减少。 拟议的研究开发的方法，应用自举的几个重要问题，在应用计量经济学的自举可能有很大的优势，替代方法，但不能令人满意地处理使用标准的自举理论。 这些是： a. 基于非光滑估计量的假设检验 B. 半参数估算器的带宽选择 C. 基于工具变量的假设检验（IV）弱工具估计。 ??