White’s Test. Breush Pagan Test. Therefore, it is able to detect more general form of heteroskedasticity than the Breusch-Pagan test. A significant chi^2 is a reason to reject the null hypothesis of homoscedasticity, i.e., indicates heteroscedasticity. The White test is used for detecting autocorrelation in a linear regression model while the Breusch-Pagan test is used for detecting heteroskedasticity. 817-838. Because of its generality, White’s test might identify specification errors other than heteroscedasticity (Thursby 1982). 4. 48, No. Tests for Heteroskedasticity White Test for Heteroskedasticitya,b,c Chi-Square df Sig. A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity Halbert White Econometrica, Vol. It has the following advantages: It does not require you to specify a model of the structure of the heteroscedasticity, if it exists. It does not depend on the assumption that the errors are normally distributed. The White Test for Heteroskedasticity White proposed to add the squares and cross products of all independent variables: 5 u = + x + x + + kxk + x + x + + kxk + 1x1x2 + 2x1x3 + + xk−1xk +v 2 2 2 2 2 0 1 … White’s test is general because it makes no assumptions about the form of the heteroscedasticity (White 1980). . Tests the null hypothesis that the variance of the errors does not depend on the values of the independent variables. For this purpose, there are a couple of tests that comes handy to establish the presence or absence of heteroscedasticity – The Breush-Pagan test and the NCV test. The white test of heteroscedasticity is a general test for the detection of heteroscdsticity existence in data set. lmtest::bptest(lmMod) # Breusch-Pagan test studentized Breusch-Pagan test data: lmMod BP = 3.2149, df = 1, p-value = 0.07297. 48 5 Heteroscedasticity and Autocorrelation 5.5.2 White Test White (1980) proposed a test for heteroscedasticity that that adds the squares and cross products of all the independent variables to Equation 5.20. (May, 1980), pp. A shortcoming of the White test is that it can lose its power very quickly particularly if the model has many regressors. The White test uses an auxiliary OLSR model in which the dependent variable is the square of the residuals from the primary model and the explanatory variables are the primary model’s explanatory … White’s Test for Heteroscedasticity is a more robust test that tests whether all the variances are equal across your data if it is not normally distributed. 30.000 14 .008 a. This could be the reason for the results such as yours. The White Test. This video explains the intuition and motivation behind the White test for heteroscedasticity. Dependent variable: GPA b. It is interpreted the same way as a chi-square test. The math is a little much for this post, but many statistical programs will calculate it for you. c. The number of regressors used in the White test is larger than the number of regressors used in the Breusch-Pagan test. Run the White test of heteroscedasticity on the residual errors.

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