This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal model of the structure of the heteroskedasticity. By comparing the elements of the new estimator to those of the usual covariance estimator, one obtains a direct test for heteroskedasticity, since in the absence of heteroskedasticity, the two estimators will be approximately equal, but will generally diverge otherwise. The test has an appealing least squares interpretation

This paper is a revised version of a paper originally en.titled "Asymptotic Properties of Nonlinear Weighted Least Squares Estimators with Independem not Identically Distributed Regressors." In Section 2, the strong consistency of a class of weighted least squares (WLS) estimators is proven under general conditions, as well as the strong consistency of weighted least squares with estimated weights (EWLS). Conditions which ensure asymptotic normality of the estimators are provided in Section 3, and a general statistic for testing hypotheses is given. In Section 4, consequences of misspecification are discussed and a test for misspecification is given. Section 5 contains a summary and concluding remarks. As should be expected, the condi- tions obtained are natural extensions of those found in the fixed regressor case. Also, the unconditional covariance matrix of the parameter estimates has a more general form than the usual conditional covariance matrix