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The Breusch–Godfrey test is a test for autocorrelation in the errors in a regression model. It makes use of the residuals from the model being considered in a regression analysis, and a test statistic is derived from these. The null hypothesis is that there is no serial correlation of any order up to p. [3]
Stata: the command estat dwatson, following regress in time series data. [6] Engle's LM test for autoregressive conditional heteroskedasticity (ARCH), a test for time-dependent volatility, the Breusch–Godfrey test, and Durbin's alternative test for serial correlation are also available.
A more flexible test, covering autocorrelation of higher orders and applicable whether or not the regressors include lags of the dependent variable, is the Breusch–Godfrey test. This involves an auxiliary regression, wherein the residuals obtained from estimating the model of interest are regressed on (a) the original regressors and (b) k ...
In Stata, one specifies the full regression, and then enters the command estat hettest followed by all independent variables. [9] [10] In SAS, Breusch–Pagan can be obtained using the Proc Model option. In Python, there is a method het_breuschpagan in statsmodels.stats.diagnostic (the statsmodels package) for Breusch–Pagan test. [11]
He is noted for the Breusch–Pagan test from the paper (with Adrian Pagan) "A simple test for heteroscedasticity and random coefficient variation" (see Noted works, below). Another contribution to econometrics is the serial correlation Lagrange multiplier test, often called Breusch–Godfrey test after Breusch and Leslie G. Godfrey , which can ...
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ADF-GLS test; Unit root tests are closely linked to serial correlation tests. However, while all processes with a unit root will exhibit serial correlation, not all serially correlated time series will have a unit root. Popular serial correlation tests include: Breusch–Godfrey test; Ljung–Box test; Durbin–Watson test
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