手法を比較

	ダービン-ワトソン検定による自己相関の検出	一般化最小二乗法 (GLS)
分野	計量経済学	統計学
系統	Regression model	Regression model
提唱年	1950	1935
提唱者	James Durbin & Geoffrey Watson	Alexander Craig Aitken
種類	Test for first-order residual autocorrelation	Linear estimator
原典	Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37(3/4), 409–428.	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
別名	DW test, Durbin-Watson statistic, Durbin-Watson otokorelasyon testi	GLS, Aitken estimator, EGLS, feasible GLS
関連	4	3
概要	The Durbin-Watson test, developed by James Durbin and Geoffrey Watson in 1950–1951, detects first-order serial correlation in the residuals of a linear regression. Its statistic ranges from 0 to 4, with a value near 2 indicating no autocorrelation, values toward 0 indicating positive autocorrelation, and values toward 4 indicating negative autocorrelation. It remains one of the most reported regression diagnostics despite well-known limitations.	Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models.