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 arima or estimate function. output a logical value indicating to print the results in R console, including the plots. The default is TRUE.

### 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 Ljung-Box 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.

Debin Qiu

### References

Engle, Robert F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50 (4): 987-1007.

McLeod, A. I. and W. K. Li. Diagnostic Checking ARMA Time Series Models Using Squared-Residual Autocorrelations. Journal of Time Series Analysis. Vol. 4, 1983, pp. 269-273.

### Examples

x <- rnorm(100)
mod <- estimate(x,p = 1) # or mod <- arima(x,order = c(1,0,0))
arch.test(mod)


[Package aTSA version 3.1.2.1 Index]