The degree of severity of heteroskedasticity and the traditional goldfeld and quandt pretest estimator
作者:
Senyo B . S. K. Adjibolosoo,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1990)
卷期:
Volume 19,
issue 3
页码: 827-836
ISSN:0361-0918
年代: 1990
DOI:10.1080/03610919008812890
出版商: Marcel Dekker, Inc.
关键词: Degree of Severity of Heteroskedasticity;Probability Density Function;Posterior Probability;Probabilistic Pretest Statistic;Monte Carlo Study
数据来源: Taylor
摘要:
The traditional Goldfeld and Quandt heteroskedasticity pretesting methodology disregards the fact that even though heteroskedasticity may exist, its degree of severity might be such that the OLS estimator would still outperform the 2SAE. It, therefore, produces results inferior to the OLS estimator when the degree of severity of heteroskedasticity is very mild. This paper through Monte Carlo simulations shows that the probabilistic pretesting procedure suggested by Adjibolosoo (1989) is more powerful than the traditional Goldfeld and Quandt heteroskedasticity pretesting methodology at the usual traditionally selected levels of significance
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