bgtest {lmtest} | R Documentation |
bgtest
performs the Breusch-Godfrey test for higher-order
serial correlation.
bgtest(formula, order = 1, order.by = NULL, type = c("Chisq", "F"), data = list())
formula |
a symbolic description for the model to be tested
(or a fitted "lm" object). |
order |
integer. maximal order of serial correlation to be tested. |
order.by |
Either a vector z or a formula with a single explanatory
variable like ~ z . The observations in the model
are ordered by the size of z . If set to NULL (the
default) the observations are assumed to be ordered (e.g., a
time series). |
type |
the type of test statistic to be returned. Either
"Chisq" for the Chi-squared test statistic or
"F" for the F test statistic. |
data |
an optional data frame containing the variables in the
model. By default the variables are taken from the environment
which bgtest is called from. |
Under H_0 the test statistic is asymptotically Chi-squared with
degrees of freedom as given in parameter
.
If type
is set to "F"
the function returns
the exact F statistic which, under H_0, follows an F
distribution with degrees of freedom as given in parameter
.
The starting values for the lagged residuals in the supplementary regression are chosen to be 0.
A list with class "htest"
containing the following components:
statistic |
the value of the test statistic. |
p.value |
the p-value of the test. |
parameter |
degrees of freedom. |
method |
a character string indicating what type of test was performed. |
data.name |
a character string giving the name(s) of the data. |
David Mitchell <david.mitchell@dotars.gov.au>, Achim Zeileis
Johnston, J. (1984): Econometric Methods, Third Edition, McGraw Hill Inc.
Godfrey, L.G. (1978): `Testing Against General Autoregressive and Moving Average Error Models when the Regressors Include Lagged Dependent Variables', Econometrica, 46, 1293-1302.
Breusch, T.S. (1979): `Testing for Autocorrelation in Dynamic Linear Models', Australian Economic Papers, 17, 334-355.
## Generate a stationary and an AR(1) series x <- rep(c(1, -1), 50) y1 <- 1 + x + rnorm(100) ## Perform Breusch-Godfrey test for first-order serial correlation: bgtest(y1 ~ x) ## or for fourth-order serial correlation bgtest(y1 ~ x, order = 4) ## Compare with Durbin-Watson test results: dwtest(y1 ~ x) y2 <- filter(y1, 0.5, method = "recursive") bgtest(y2 ~ x)