Bootstrapping a Regression Equation: Some Empirical Results
Bootstrapping a Regression Equation: Some Empirical Results
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DOI：
10.1080/01621459.1984.10477069
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发表时间：
1984-03
影响因子：
3.7
通讯作者：
D. Freedman;S. Peters
中科院分区：
文献类型：
--
作者：
D. Freedman;S. Peters
摘要自助法和折叠法一样，是一种估计标准误差的技术。其思想是使用基于潜在误差分布的非参数估计的蒙特卡罗模拟。这篇文章的主要目的是在描述工业能源需求的计量经济学方程的背景下提出自助法。结果表明，当应用于特定的有限样本问题时，传统的估计标准误差的渐近公式过于乐观，几乎是原来的三倍。在更简单的情况下，这一发现可以得到数学证明。
Abstract The bootstrap, like the jackknife, is a technique for estimating standard errors. The idea is to use Monte Carlo simulation based on a nonparametric estimate of the underlying error distribution. The main object of this article is to present the bootstrap in the context of an econometric equation describing the demand for energy by industry. As it turns out, the conventional asymptotic formulas for estimating standard errors are too optimistic by factors of nearly three, when applied to a particular finite-sample problem. In a simpler context, this finding can be given a mathematical proof.