Methoden vergleichen

	Goldfeld-Quandt-Test auf Heteroskedastizität	Breusch-Pagan-Test auf Heteroskedastizität	Gewichtete Kleinste Quadrate (GKS)
Fachgebiet	Ökonometrie	Ökonometrie	Statistik
Familie	Hypothesis test	Regression model	Regression model
Entstehungsjahr	1965	1979	1935
Urheber	Stephen Goldfeld & Richard Quandt	Trevor Breusch & Adrian Pagan	Alexander Craig Aitken
Typ	F-ratio test for heteroskedasticity	Lagrange-multiplier test for heteroskedasticity	Weighted linear estimator
Wegweisende Quelle	Goldfeld, S. M., & Quandt, R. E. (1965). Some tests for homoscedasticity. Journal of the American Statistical Association, 60(310), 539–547.	Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47(5), 1287–1294.	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
Aliasnamen	GQ Test, Goldfeld-Quandt Heteroskedasticity Test, Split-Sample Variance Ratio Test, Goldfeld-Quandt Homojenlik Testi	BP test, Breusch-Pagan-Godfrey test, Lagrange multiplier test for heteroskedasticity, Breusch-Pagan değişen varyans testi	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares
Verwandt	3	3	3
Zusammenfassung	The Goldfeld-Quandt test, introduced by Stephen Goldfeld and Richard Quandt in 1965, is a classical diagnostic procedure for detecting heteroskedasticity in OLS regression. It operates by sorting observations according to a variable suspected of driving variance, omitting a central block, fitting separate regressions on the two tail sub-samples, and comparing their residual variances via an F-ratio. The test is particularly well-suited to situations where the error variance is believed to increase or decrease monotonically with an observed regressor.	The Breusch-Pagan test, introduced by Trevor Breusch and Adrian Pagan in 1979, is a Lagrange-multiplier test for heteroskedasticity — the condition where the variance of a regression's errors changes with the explanatory variables. It works by regressing the squared OLS residuals on candidate variables and checking whether they explain any of the residual variation, signalling that the constant-variance assumption is violated.	Weighted Least Squares is a generalization of Ordinary Least Squares (OLS) regression that assigns each observation a weight inversely proportional to its error variance, thereby down-weighting high-variance data points and up-weighting precise ones. Introduced in its general matrix form by Alexander Craig Aitken in 1935, WLS is the canonical remedy when heteroscedasticity is present and the error variance structure is known or can be reliably estimated.