##Simulate from an AR(1):
set.seed(123)
y <- arima.sim(list(ar=0.4), 100)
##Simulate four independent Gaussian regressors:
xregs <- matrix(rnorm(4*100), 100, 4)
##estimate an AR(2) with intercept and four conditioning
##regressors in the mean, and a log-ARCH(3) in the variance:
mymod <- arx(y, mc=TRUE, ar=1:2, mxreg=xregs, arch=1:3)
##General-to-Specific (GETS) model selection of the mean:
meanmod <- getsm(mymod)
##General-to-Specific (GETS) model selection of the variance:
varmod <- getsv(mymod)
##print results:
print(meanmod)
print(varmod)
##plot the fitted vs. actual values, and the residuals:
plot(meanmod)
plot(varmod)
##print the entries of object 'gets':
summary(meanmod)
summary(varmod)
##extract coefficients of the simplified (specific) model:
coef(meanmod) #mean spec
coef(varmod) #variance spec
##extract log-likelihood:
logLik(mymod)
##extract variance-covariance matrix of simplified
##(specific) model:
vcov(meanmod) #mean spec
vcov(varmod) #variance spec
##extract and plot the fitted values:
mfit <- fitted(meanmod) #mean fit
plot(mfit)
vfit <- fitted(varmod) #variance fit
plot(vfit)
##extract and plot residuals:
epshat <- residuals(meanmod)
plot(epshat)
##extract and plot standardised residuals:
zhat <- residuals(varmod)
plot(zhat)
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