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首页> 外文期刊>Journal of statistical computation and simulation >Detecting heteroscedasticity in a simple regression model via quantile regression slopes
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Detecting heteroscedasticity in a simple regression model via quantile regression slopes

机译:通过分位数回归斜率在简单回归模型中检测异方差

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摘要

Consider the linear regression model Y = βX_1 + α + τ(X)ε, where X and ε are independent random variables, ε has a mean of zero and variance σ~2, and τ is some unknown function used to model heteroscedasticity. Many methods have been proposed for testing H_0: τ(X) ≡ 1, the hypothesis that the error term is homoscedastic, with most methods known to be unsatisfactory in terms of controlling the probability of a Type Ⅰ error. This paper considers several approaches based on a quantile regression estimator, one of which (method N2) is recommended for general use. A minor goal is to report new results related to a method suggested by Koenker. Method N2 does not dominate Koenker's method in terms of power, but as illustrated, the choice of method can make a considerable difference when testing H_0. In particular, situations occur where Koenker's method is highly non-significant, yet method N2 rejects at the 0.01 level.
机译:考虑线性回归模型Y =βX_1+α+τ(X)ε,其中X和ε是独立的随机变量,ε的平均值为零,方差为σ〜2,而τ是用于建模异方差的一些未知函数。已经提出了许多测试H_0的方法:τ(X)≡1,即误差项是同调的,大多数已知的方法在控制Ⅰ类误差的可能性方面都不令人满意。本文考虑了基于分位数回归估计量的几种方法,其中一种(方法N2)推荐用于一般用途。一个次要目标是报告与Koenker建议的方法有关的新结果。方法N2在功率方面并不能控制Koenker方法,但是如图所示,在测试H_0时,方法的选择会产生很大的不同。尤其是在发生这样的情况时,Koenker方法的重要性不高,而方法N2的拒绝水平为0.01。

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