Сравнение методов

	Тест Бре́ша	Регрессия методом обыкновенных наименьших квадратов (ОНМК)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1978	2019
Автор метода	Trevor Breusch & Leslie Godfrey	Wooldridge (textbook treatment); classical least squares
Тип	Lagrange-multiplier test for serial correlation	Linear regression
Основополагающий источник	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Другие названия	BG test, LM test for autocorrelation, Breusch-Godfrey serial correlation test, Breusch-Godfrey otokorelasyon testi	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Связанные	3	5
Сводка	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.	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).