Showing papers by "Halbert White published in 1980"

A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity

Abstract: 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.

Some Large-Sample Tests for Nonnormality in the Linear Regression Model

Abstract: This study provides conditions under which several well-known and easily computable statistics for testing nonnormality (√b 1, b 2, D, W′, W) can be modified for large-sample use in the classical linear regression framework by replacing the true stochastic error with the least squares residual. Monte Carlo simulations indicate that the modified statistics perform acceptably well even for n = 35. None of the tests clearly dominates the others, although the modified version of W′ performs well for moderate sample sizes, while the modified D and omnibus R test, based on joint use of √b 1 and b 2, perform well for larger samples (n = 50, 100). We illustrate the use of normality tests using a recent empirical study by Lillard and Willis.