On inference in the presence of heteroskedasticity without replicated observations
作者:
Walter Sudmant,
Peter Kennedy,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1990)
卷期:
Volume 19,
issue 2
页码: 491-504
ISSN:0361-0918
年代: 1990
DOI:10.1080/03610919008812870
出版商: Marcel Dekker, Inc.
关键词: size;power;test;pretest estimator;estimated generalized least squares;jackknife
数据来源: Taylor
摘要:
A Monte Carlo study is used to examine the size and power of t tests formed using a variety of estimation procedures appropriate in the context of heteroskedasticity when there are no replicated observations. There are three main results: (1) the ordinary least squares estimator is quite robust with respect to inference; (2) an estimated generalized least squares estimator, formed using a possibly-erroneous assumption that the functional form of the heteroskedasticity is multiplicative, has highest power among the estimators considered, but has a too-large size; and (3) the advantages of the jackknife do not appear until the degree of heteroskedasticity is unrealistically large
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