Contents
What is the null hypothesis of White test?
The null hypothesis for White’s test is that the variances for the errors are equal. In math terms, that’s: H0 = σ2i = σ2.
Why do we check for Homoscedasticity?
Homoscedasticity, or homogeneity of variances, is an assumption of equal or similar variances in different groups being compared. This is an important assumption of parametric statistical tests because they are sensitive to any dissimilarities. Uneven variances in samples result in biased and skewed test results.
How to test whether a given time series is just white ‐ noise?
How do I test whether a given time series is just white‐noise? In a statistical sense, a time series xt is characterized as having a weak white test in Excel (white noise) if xt is a sequence of serially uncorrelated random variables with zero mean and finite variance.
How does the White test for homoscedasticity work?
Both White’s test and the Breusch-Pagan test are based on the residuals of the fitted model. To test the assumption of homoscedasticity, one can use auxiliary regression analysis by regressing the squared residuals from the original model on the set of original regressors, the cross-products of the regressors, and the squared regressors.
Is the spike at x = 500 white noise?
In your case the spike at x = 500 gets averaged out in the sample autocorrelations & hardly affects the test statistic at all (check the auto-correlation plot with acf ). This time series isn’t white noise because its mean is far from zero (do a t -test if you like).
Why is the White test for heteroskedasticity general?
White test for Heteroskedasticity is general because it do not rely on the normality assumptions and it is also easy to implement. Because of the generality of White’s test, it may identify the specification bias too. Both White’s test and the Breusch-Pagan test are based on the residuals of the fitted model.