n <- 1000 # Length of simulated time series
e <- rnorm(n) # Generate ARCH(1) process
x <- double(n)
x[1] <- rnorm(1)
for(i in 2:n) {
x[i] <- e[i] * sqrt(0.1+0.4*x[i-1]^2)
}
x <- ts(x)
plot(x)
# Each test takes about 3 sec on a Pentium II 300MHz
amif(x, lag.max=5) # i.i.d. vs. any dependence
amif(x, lag.max=5, fft=TRUE) # linear vs. non-linear
amif(x, lag.max=5, fft=TRUE, amplitude=TRUE)
e <- rnorm(n) # Generate AR(1) process
x <- double(n)
x[1] <- rnorm(1)
for(i in 2:n)
{
x[i] <- 0.4*x[i-1]+e[i]
}
x <- ts(x)
plot(x)
amif(x, lag.max=5) # i.i.d. vs. any dependence
amif(x, lag.max=5, fft=TRUE) # linear vs. non-linear
amif(x, lag.max=5, fft=TRUE, amplitude=TRUE)
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