Instructions: In this task, you are given an abstract of article. Your task is to generate title for this article. Preferred titles are under thirty words.
Input: 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.
Output:
A Heteroskedasticity-Consistent Covariance Matrix Estimator And A Direct Test For Heteroskedasticity