The degree of severity of heteroskedasticity and the traditional goldfeld and quandt pretest estimator

Abstract

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

Keywords:

• Degree of Severity of Heteroskedasticity
• Probability Density Function
• Posterior Probability
• Probabilistic Pretest Statistic
• Monte Carlo Study