White test

In statistics, the White test, named after Halbert White, is a test that establishes whether the residual variance of a variable in a regression model is constant (homoscedasticity). To test for constant variance one regresses the squared residuals from a regression model onto the regressors, the cross-products of the regressors and the squared regressors. One then inspects the R². If homoskedasticity is rejected one can use a GARCH model.

Today, standard errors that are correct in the presence of heteroskedasticity are widely used. Reflecting this, the paper that published White's test and a formula for such standard errors, "A Heteroskedasticity—Consistent Covariance Matrix Estimator and a Direct Test for Hetereoskedasticity” (1980) is one of the most cited articles in Economics journals ^[1].

The LM test statistic is the product of the R² value and sample size. It follows a chi square distribution, with degrees of freedom equal to one less than the number of independent variables.

See also

• Breusch–Pagan test
• Heteroscedasticity
• Heteroscedasticity-consistent standard errors

References

1. ^ "Dr. Halbert White paper most cited in economics literature since 1970", Bates White consulting firm

• White, Halbert (1980) "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity". Econometrica, 48, 817–838

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