Description Usage Arguments Details Value Note Author(s) References Examples
Performs Portmanteau Q and Lagrange Multiplier tests for the null hypothesis that the residuals of a ARIMA model are homoscedastic.
1 
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 
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
.
A matrix with the following five columns:

the lag parameter to calculate the test statistics. 

the Portmanteau Q test statistic. 

the p.value for PQ test. 

the Lagrange Multiplier test statistic. 

the p.value for LM test. 
Missing values are removed before analysis.
Debin Qiu
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.
1 2 3 
Attaching package: 'aTSA'
The following object is masked from 'package:graphics':
identify
ARIMA(1,0,0) model is estimated for variable: x
ConditionalSumofSquares & Maximum Likelihood Estimation
Estimate S.E t.value p.value Lag
MU 0.145 0.1117 1.30 0.197 1
AR 1 0.140 0.0987 1.42 0.158 1

n = 100; 'sigma' = 0.9615573; AIC = 281.9674; SBC = 287.1778

Correlation of Parameter Estimates
MU AR 1
MU 1.0000 0.0104
AR 1 0.0104 1.0000

Autocorrelation Check of Residuals
lag LB p.value
[1,] 4 2.25 0.689
[2,] 8 3.82 0.873
[3,] 12 10.85 0.542
[4,] 16 13.83 0.611
[5,] 20 14.31 0.814
[6,] 24 16.60 0.865

Model for variable: x
Estimated mean: 0.1451695
AR factors: 1 + 0.1405 B**(1)
ARCH heteroscedasticity test for residuals
alternative: heteroscedastic
PortmanteauQ test:
order PQ p.value
[1,] 4 4.33 0.364
[2,] 8 10.77 0.215
[3,] 12 13.31 0.347
[4,] 16 18.21 0.312
[5,] 20 20.70 0.415
[6,] 24 29.32 0.208
LagrangeMultiplier test:
order LM p.value
[1,] 4 13.374 0.00389
[2,] 8 5.597 0.58754
[3,] 12 2.420 0.99639
[4,] 16 1.084 1.00000
[5,] 20 0.578 1.00000
[6,] 24 0.281 1.00000
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