Sammenlign metoder

	Breusch-Godfrey LM-test for seriel korrelation	ARIMA (Autoregressive Integrated Moving Average) Model
Fagområde	Økonometri	Økonometri
Familie	Regression model	Regression model
Oprindelsesår	1978	2015
Ophavsperson	Trevor Breusch & Leslie Godfrey	Box & Jenkins (Box-Jenkins methodology)
Type	Lagrange-multiplier test for serial correlation	Univariate time-series model
Oprindelig kilde	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.	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021
Aliasser	BG test, LM test for autocorrelation, Breusch-Godfrey serial correlation test, Breusch-Godfrey otokorelasyon testi	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
Relaterede	3	5
Resumé	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.	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).