Comparar métodos

	Contraste LM de Breusch-Godfrey para autocorrelación serial	Prueba de Durbin-Watson para Autocorrelación	Regresión por Mínimos Cuadrados Ordinarios (MCO)
Campo	Econometría	Econometría	Econometría
Familia	Regression model	Regression model	Regression model
Año de origen	1978	1950	2019
Autor original	Trevor Breusch & Leslie Godfrey	James Durbin & Geoffrey Watson	Wooldridge (textbook treatment); classical least squares
Tipo	Lagrange-multiplier test for serial correlation	Test for first-order residual autocorrelation	Linear regression
Fuente seminal	Godfrey, L. G. (1978). Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables. Econometrica, 46(6), 1293–1301.	Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37(3/4), 409–428.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	BG test, LM test for autocorrelation, Breusch-Godfrey serial correlation test, Breusch-Godfrey otokorelasyon testi	DW test, Durbin-Watson statistic, Durbin-Watson otokorelasyon testi	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relacionados	3	4	5
Resumen	The Breusch-Godfrey test is a Lagrange-multiplier test for serial correlation in regression residuals, developed independently by Trevor Breusch (1978) and Leslie Godfrey (1978). Unlike the Durbin-Watson test, it detects autocorrelation up to any chosen order p, remains valid when the model includes lagged dependent variables, and produces a definite chi-square p-value rather than an inconclusive region — making it the modern standard for autocorrelation testing.	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).