Compute the Box--Pierce or Ljung--Box test statistic for examining the null hypothesis of independence in a given time series. These are sometimes known as ‘portmanteau’ tests.
Box.test(x, lag = 1, type = c("Box-Pierce", "Ljung-Box"), fitdf = 0)
- a numeric vector or univariate time series.
- the statistic will be based on
- test to be performed: partial matching is used.
- number of degrees of freedom to be subtracted if
xis a series of residuals.
These tests are sometimes applied to the residuals from an
ARMA(p, q) fit, in which case the references suggest a better approximation to the null-hypothesis distribution is obtained by setting
fitdf = p+q, provided of course that
lag > fitdf.
A list with class
"htest" containing the following components:
- the value of the test statistic.
- the degrees of freedom of the approximate chi-squared distribution of the test statistic (taking
- the p-value of the test.
- a character string indicating which type of test was performed.
- a character string giving the name of the data.
Box, G. E. P. and Pierce, D. A. (1970), Distribution of residual correlations in autoregressive-integrated moving average time series models. Journal of the American Statistical Association, 65, 1509--1526.
Ljung, G. M. and Box, G. E. P. (1978), On a measure of lack of fit in time series models. Biometrika 65, 297--303.
Harvey, A. C. (1993) Time Series Models. 2nd Edition, Harvester Wheatsheaf, NY, pp. 44, 45.
Missing values are not handled.
Documentation reproduced from R 3.0.2. License: GPL-2.