mixAR_sim | R Documentation |
Simulate from MixAR models
mixAR_sim(model, n, init, nskip = 100, flag = FALSE) mixAny_sim(model, n, init, nskip=100, flag = FALSE, theta, galpha0, galpha, gbeta)
model |
model from which to simulate, an object inheriting from class |
init |
initial values, numeric vector. |
n |
size of the simulated series. |
nskip |
number of burn-in values, see Details. |
flag |
if |
theta |
ma coef, a list. |
galpha0 |
alpha0[k], k=1,...,g. |
galpha |
garch alpha. |
gbeta |
garch beta. |
mixAR_sim
simulates a series of length nskip+n
and
returns the last n
values.
mixAny_sim
simulates from a MixAR model with GARCH
innovations. mixAny_sim
was a quick fix for Shahadat and needs
consolidation.
The vector init
provides the initial values for
t=...,-1,0. Its length must be at least equal to the maximal AR
order. If it is longer, only the last max(model@order)
elements
are used.
a numeric vector of length n
. If flag = TRUE
it has
attribute regimes
containing z
.
exampleModels$WL_ibm ## simulate a continuation of BJ ibm data ts1 <- mixAR_sim(exampleModels$WL_ibm, n = 30, init = c(346, 352, 357), nskip = 0) # a simulation based estimate of the 1-step predictive distribution # for the first date after the data. s1 <- replicate(1000, mixAR_sim(exampleModels$WL_ibm, n = 1, init = c(346, 352, 357), nskip = 0)) plot(density(s1)) # load ibm data from BJ ## data(ibmclose, package = "fma") # overlay the 'true' predictive density. pdf1 <- mix_pdf(exampleModels$WL_ibm, xcond = as.numeric(fma::ibmclose)) curve(pdf1, add = TRUE, col = 'blue') # estimate of 5% quantile of predictive distribution quantile(s1, 0.05) # Monte Carlo estimate of "expected shortfall" # (but the data has not been converted into returns...) mean(s1[ s1 <= quantile(s1, 0.05) ])
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