arch.test {aTSA}  R Documentation 
ARCH Engle's Test for Residual Heteroscedasticity
Description
Performs Portmanteau Q and Lagrange Multiplier tests for the null hypothesis that the residuals of a ARIMA model are homoscedastic.
Usage
arch.test(object, output = TRUE)
Arguments
object 
an object from arima model estimated by

output 
a logical value indicating to print the results in R console, including the
plots. The default is 
Details
The ARCH Engle's test is constructed based on the fact that if the residuals
(defined as e[t]
) are heteroscedastic, the squared residuals (e^2[t]
) are
autocorrelated. The
first type of test is to examine whether the squares of residuals are a sequence of white
noise, which is called Portmanteau Q test and similar to the LjungBox test on the squared
residuals. The second type of test proposed by Engle (1982) is the Lagrange Multiplier
test which is to fit a linear regression model for the squared residuals and examine
whether the fitted model
is significant. So the null hypothesis is that the squared residuals are a sequence
of white noise, namely, the residuals are homoscedastic. The lag parameter
to calculate the test statistics is taken from an integer sequence of 1:min(24,n)
with
step 4 if n > 25
, otherwise 2, where n
is the number of nonmissing observations.
The plots of residuals, squared residuals, p.values of PQ and LM tests will be drawn if
output = TRUE
.
Value
A matrix with the following five columns:
order 
the lag parameter to calculate the test statistics. 
PQ 
the Portmanteau Q test statistic. 
p.value 
the p.value for PQ test. 
LM 
the Lagrange Multiplier test statistic. 
p.value 
the p.value for LM test. 
Note
Missing values are removed before analysis.
Author(s)
Debin Qiu
References
Engle, Robert F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50 (4): 9871007.
McLeod, A. I. and W. K. Li. Diagnostic Checking ARMA Time Series Models Using SquaredResidual Autocorrelations. Journal of Time Series Analysis. Vol. 4, 1983, pp. 269273.
Examples
x < rnorm(100)
mod < estimate(x,p = 1) # or mod < arima(x,order = c(1,0,0))
arch.test(mod)