marma(n, p = 0, q = 0, psi, theta, init = rep(0, p), n.start = p,
rand.gen = rfrechet, ...)
mar(n, p = 1, psi, init = rep(0, p), n.start = p, rand.gen =
rfrechet, ...)
mma(n, q = 1, theta, rand.gen = rfrechet, ...)
p
. Can be omitted if p
is zero.q
. Can be omitted if q
is zero.p
.n.start
is less than p
, then
p
minus n.start
starting values will be included
in the output series.rand.gen
. Most
usefully, the scale and shape parameters of the innovations
generated by rfrechet
can be specified by scale
and shape
respectively.n
.rand.gen
. The functions mar
and mma
generate MAR(p) and
MMA(q) processes respectively.
A MAR(p) process ${X_k}$ is equivalent to a
MARMA(p, 0) process, so that
$$X_k = \max{\phi_1 X_{k-1}, \ldots, \phi_p X_{k-p},
\epsilon_k}.$$
A MMA(q) process ${X_k}$ is equivalent to a
MARMA(0, q) process, so that
$$X_k = \max{\epsilon_k, \theta_1 \epsilon_{k-1}, \ldots,
\theta_q \epsilon_{k-q}}.$$
evmc
marma(100, p = 1, q = 1, psi = 0.75, theta = 0.65)
mar(100, psi = 0.85, n.start = 20)
mma(100, q = 2, theta = c(0.75, 0.8))
Run the code above in your browser using DataLab