Goldfeld–Quandt Test
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A Goldfeld–Quandt Test is a statistical testing of homoscedasticity in regression analyses.
- See: Homoscedasticity Statistical Deviation, Statistical Dispersion, Statistical Test, Regression Analysis.
References
2016
- (Wikipedia, 2016) ⇒ http://en.wikipedia.org/wiki/Goldfeld-Quandt_test Retrieved 2016-08-21
- In statistics, the Goldfeld–Quandt test checks for homoscedasticity in regression analyses. It does this by dividing a dataset into two parts or groups, and hence the test is sometimes called a two-group test. The Goldfeld–Quandt test is one of two tests proposed in a 1965 paper by Stephen Goldfeld and Richard Quandt. Both a parametric and nonparametric test are described in the paper, but the term "Goldfeld–Quandt test" is usually associated only with the former.
- 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.