portes (version 1.08)

LiMcLeod: The Modified Multivariate Portmanteau Test, Li-McLeod (1981)

Description

The modified multivariate portmanteau test suggested by Li and McLeod (1981).

Usage

LiMcLeod(obj,lags=seq(5,30,5),order=0,SquaredQ=FALSE)

Arguments

obj

a univariate or multivariate series with class "numeric", "matrix", "ts", or ("mts" "ts"). It can be also an object of fitted time-series model with class "ar",

lags

vector of lag auto-cross correlation coefficients used for LiMcLeod test.

order

this is used for degrees of freedom of asymptotic chi-square distribution. If obj is a fitted time-series model with class "ar", "arima0", "Arima", "varest",

SquaredQ

if TRUE then apply the test on the squared values. This checks for Autoregressive Conditional Heteroscedastic, ARCH, effects. When SquaredQ = FALSE, then apply the test on the usual resi

Value

• The multivariate test statistic suggested by Li and McLeod (1981) and its corresponding p-values for different lags based on the asymptotic chi-square distribution with k^2(lags-order) degrees of freedom.

References

Li, W. K. and McLeod, A. I. (1981). "Distribution of The Residual Autocorrelations in Multivariate ARMA Time Series Models". Journal of The Royal Statistical Society, Series B, 43, 231-239.

See Also

acf, Box.test, BoxPierce, LjungBox, Hosking, gvtest, portest, GetResiduals

Examples

##############################################################
## Quarterly, west German investment, income, and consumption 
## from first quarter of 1960 to fourth quarter of 1982: 
##############################################################
data(WestGerman)
DiffData <- matrix(numeric(3 * 91), ncol = 3)
  for (i in 1:3) 
    DiffData[, i] <- diff(log(WestGerman[, i]), lag = 1)
fit <- ar.ols(DiffData, intercept = TRUE, order.max = 2)
lags <- c(5,10)
## Apply the test statistic on the fitted model 
LiMcLeod(fit,lags,order = 2)        ## True
LiMcLeod(fit,lags)                  ## True
## Apply the test statistic on the residuals
res <- ts((fit$resid)[-(1:2), ])
LiMcLeod(res,lags,order = 2)        ## True
LiMcLeod(res,lags)                  ## Not True