This paper dwells on the choice between the ordinary least squares and the estimated generalized least squares estimators when the presence of heteroskedasticity is suspected. Since the estimated generalized least squares estimator does not dominate the ordinary least squares estimator completely over the whole parameter space. it is of interest to the researcher to know in advance whether the degree of severity of heteroskedasticity is such that OLS estimator outperforms the estimated generalized least squares [or ZSAE!. Casting the problem in the non-spherical error mold and exploiting the principle underlying the Bayesian pretest estimator, an intuitive non-mathematical procedure is proposed to serve a s a n aid to the researcher in deciding when to use either the ordinary least squares (OLS) or the estimated generalized least squares (2SAE) estimators. 3451

Even though it is widely known c hat heteroskedasticity reduces the efficiency of t,he OLS estimator, very little is known about what the impact trends have upon the power and robustness (to different trends in the data) of tests for heteroskedasticity. Research in this area fails to build the degree of severity of heteroskedasticity into tests designed to detect its presence. This paper evaluates the impacts of various trends in the regressors on the power and robustness of tests for heteroskedasticity through Monte Carlo studies and suggests that the power of each test increases when the degree of severity of'heteroskedasticity is built into the test statislic. 927

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