Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Prueba de Durbin-Watson para Autocorrelación× | Mínimos Cuadrados Generalizados (GLS)× | |
|---|---|---|
| Campo≠ | Econometría | Estadística |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1950 | 1935 |
| Autor original≠ | James Durbin & Geoffrey Watson | Alexander Craig Aitken |
| Tipo≠ | Test for first-order residual autocorrelation | Linear estimator |
| Fuente seminal≠ | Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37(3/4), 409–428. DOI ↗ | Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48. DOI ↗ |
| Alias≠ | DW test, Durbin-Watson statistic, Durbin-Watson otokorelasyon testi | GLS, Aitken estimator, EGLS, feasible GLS |
| Relacionados≠ | 4 | 3 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
|
|