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Heteroscedasticity often occurs when there is a large difference among the sizes of the observations. A classic example of heteroscedasticity is that of income versus expenditure on meals. A wealthy person may eat inexpe
In statistics, the Glejser test for heteroscedasticity, developed in 1969 by Herbert Glejser, regresses the residuals on the explanatory variable that is thought to be related to the heteroscedastic variance. [1]
In statistics, the Breusch–Pagan test, developed in 1979 by Trevor Breusch and Adrian Pagan, [1] is used to test for heteroskedasticity in a linear regression model. It was independently suggested with some extension by R. Dennis Cook and Sanford Weisberg in 1983 (Cook–Weisberg test). [2]
In R, White's Test can be implemented using the white function of the skedastic package. [5]In Python, White's Test can be implemented using the het_white function of the statsmodels.stats.diagnostic.het_white [6]
This page was last edited on 16 January 2025, at 09:54 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.
Herbert Glejser, in his 1969 paper outlining the Glejser test, provides a small sampling experiment to test the power and sensitivity of the Goldfeld–Quandt test. His results show limited success for the Goldfeld–Quandt test except under cases of "pure heteroskedasticity"—where variance can be described as a function of only the underlying explanatory variable.
In statistics, homogeneity and its opposite, heterogeneity, arise in describing the properties of a dataset, or several datasets.They relate to the validity of the often convenient assumption that the statistical properties of any one part of an overall dataset are the same as any other part.
The procedure for the ADF test is the same as for the Dickey–Fuller test but it is applied to the model = + + + + + + +, where is a constant, the coefficient on a time trend and the lag order of the autoregressive process.