Goldfeld–Quandt Test

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A Goldfeld–Quandt Test is a statistical testing of homoscedasticity in regression analyses.



References

2016

Test: The nonparametric test can be visualized by comparing the number of 'peaks' in the residuals from a regression ordered against a pre-identified variable with how many peaks would arise randomly. The lower figure is provided only for comparison, no part of the test involves visual comparison with a hypothetical homoskedastic error structure.
In the context of multiple regression (or univariate regression), the hypothesis to be tested is that the variances of the errors of the regression model are not constant, but instead are monotonically related to a pre-identified explanatory variable. For example, data on income and consumption may be gathered and consumption regressed against income. If the variance increases as levels of income increase, then income may be used as an explanatory variable. Otherwise some third variable (e.g. wealth or last period income) may be chosen.