手法を比較

	不均一分散のゴールドフェルド・クワント検定	加重最小二乗法 (WLS)	Whiteの不均一分散検定
分野	計量経済学	統計学	計量経済学
系統	Hypothesis test	Regression model	Regression model
提唱年	1965	1935	1980
提唱者	Stephen Goldfeld & Richard Quandt	Alexander Craig Aitken	Halbert White
種類	F-ratio test for heteroskedasticity	Weighted linear estimator	General test for heteroskedasticity
原典	Goldfeld, S. M., & Quandt, R. E. (1965). Some tests for homoscedasticity. Journal of the American Statistical Association, 60(310), 539–547.	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
別名	GQ Test, Goldfeld-Quandt Heteroskedasticity Test, Split-Sample Variance Ratio Test, Goldfeld-Quandt Homojenlik Testi	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares	White's general heteroskedasticity test, White değişen varyans testi
関連	3	3	3
概要	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.	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.	The White test, introduced by Halbert White in 1980, is a general test for heteroskedasticity that makes no assumption about its functional form. It regresses the squared OLS residuals on the regressors, their squares, and their cross-products, so it can detect heteroskedasticity related to any of these terms. The same 1980 paper introduced the heteroskedasticity-consistent ('White') standard errors that are the standard remedy when the test rejects.