Abstract

This study focuses on examining the power of bootstrapped and exact tests to detect variance heterogeneity in linear models via Monte Carlo. Three independent MC schemes were set up corresponding to distinct forms of heteroskedasticity, degree of heteroskedasticity and with or without normality. The tests produced unequal power in the simulated combinations; some performed poorly, when exposed to the unusual type of heteroskedasticity from the point of the test. The Bamset LR test had adequate power for most skedastic functions and should be extensively used. Park’s test had inadequate power in most scenarios. The power of White’s test varied strongly depending on the skedastic function. The bootstrapped tests had generally lower power and were often oversized, although the only resampled Harrison and McCabe test had outstanding power in several MC schemes. Reduced power and increased Type I Error Rates were noticed in most exact or resampled tests with skewed disturbances.

摘要

本研究的重点是检查引导和精确测试通过蒙特卡罗检测线性模型中方差异质性的能力。根据异方差的不同形式、异方差的程度以及有或没有正态性，建立了三个独立的MC方案。测试在模拟组合中产生了不相等的功率；从测试的角度来看，当暴露于不寻常类型的异方差性时，有些表现不佳。Bamset LR 检验对于大多数 skedastic 函数具有足够的功效，应该广泛使用。Park 的测试在大多数情况下都不够有效。怀特检验的功效根据偏差函数的不同而有很大差异。自举测试通常具有较低的功率并且通常规模过大，尽管唯一的重新采样的 Harrison 和 McCabe 测试在多个 MC 方案中具有出色的能力。在大多数具有倾斜干扰的精确或重采样测试中，我们注意到功率降低和 I 类错误率增加。