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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 R^{2}. If homoskedasticity is rejected one can use a GARCH model.

An interesting fact is that the paper that published White's test, "A Heteroskedasticity—Consistent Covariance Matrix Estimator and a Direct Test for Hetereoskedasticity” (1980) is the one of the most cited articles in Economics journals [1].

The LM test statistic is the product of the  R^{2} value and sample size. It follows a chi square distribution, with degrees of freedom equal to one less than the number of independent variables.

\ LM = n \cdot R^2

See alsoEdit


  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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