| arfimafit-methods {rugarch} | R Documentation |
Method for fitting an ARFIMA models.
arfimafit(spec, data, out.sample = 0, solver = "solnp", solver.control = list(), fit.control = list(fixed.se = 0, scale = 0), ...)
data |
A univariate data object. Can be a numeric vector, matrix, data.frame, zoo, xts, timeSeries, ts or irts object. |
spec |
An ARFIMA spec object of class |
out.sample |
A positive integer indicating the number of periods before the last to keep for out of sample forecasting (see details). |
solver |
One of either “nlminb”, “solnp”, “gosolnp” or “nloptr”. |
solver.control |
Control arguments list passed to optimizer. |
fit.control |
Control arguments passed to the fitting routine. The
fixed.se argument controls whether standard errors should be calculated for
those parameters which were fixed (through the fixed.pars argument of the
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The ARFIMA optimization routine first calculates a set of feasible starting
points which are used to initiate the ARFIMA Maximum Likelihood recursion. The
main part of the likelihood calculation is performed in C-code for speed.
The out.sample option is provided in order to carry out forecast performance
testing against actual data. A minimum of 5 data points are required for these
tests. If the out.sample option is positive, then the routine will fit only
N - out.sample (where N is the total data length) data points, leaving
out.sample points for forecasting and testing using the forecast performance
measures. In the arfimaforecast routine the
n.ahead may also be greater than the out.sample number resulting in a
combination of out of sample data points matched against actual data and some
without, which the forecast performance tests will ignore.
The “gosolnp” solver allows for the initialization of multiple restarts
of the solnp solver with randomly generated parameters (see documentation in
the Rsolnp-package for details of the strategy used). The solver.control list
then accepts the following additional (to the solnp) arguments: “n.restarts”
is the number of solver restarts required (defaults to 1), “parallel” and
“parallel.control” for use of the parallel functionality, “rseed”
is the seed to initialize the random number generator, and “n.sim” is the
number of simulated parameter vectors to generate per n.restarts.
A ARFIMAfit object containing details of the ARFIMA fit.
Alexios Ghalanos
## Not run:
data(sp500ret)
fit = vector(mode = "list", length = 9)
dist = c("norm", "snorm", "std", "sstd", "ged", "sged", "nig", "ghyp", "jsu")
for(i in 1:9){
spec = arfimaspec(mean.model = list(armaOrder = c(1,1), include.mean = TRUE,
arfima = FALSE), distribution.model = dist[i])
fit[[i]] = arfimafit(spec = spec, data = sp500ret, solver = "solnp",
fit.control = list(scale = 1))
}
cfmatrix = matrix(NA, nrow = 9, ncol = 7)
colnames(cfmatrix) = c("mu", "ar1", "ma1", "sigma", "skew", "shape", "ghlambda")
rownames(cfmatrix) = dist
for(i in 1:9){
cf = coef(fit[[i]])
cfmatrix[i, match(names(cf), colnames(cfmatrix))] = cf
}
sk = ku = rep(0, 9)
for(i in 1:9){
cf = coef(fit[[i]])
if(fit[[i]]@model$modelinc[16]>0) sk[i] = dskewness(distribution = dist[i],
skew = cf["skew"], shape = cf["shape"], lambda = cf["ghlambda"])
if(fit[[i]]@model$modelinc[17]>0) ku[i] = dkurtosis(distribution = dist[i],
skew = cf["skew"], shape = cf["shape"], lambda = cf["ghlambda"])
}
hq = sapply(fit, FUN = function(x) infocriteria(x)[4])
cfmatrix = cbind(cfmatrix, sk, ku, hq)
colnames(cfmatrix)=c(colnames(cfmatrix[,1:7]), "skewness", "ex.kurtosis","HQIC")
print(cfmatrix, digits = 4)
# notice that for the student distribution kurtosis is NA since shape (dof) < 4.
cat("\nas.data.frame method:\n")
head(as.data.frame(fit[[1]]))
cat("\ncoef method:\n")
coef(fit[[1]])
cat("\nfitted method:\n")
head(fitted(fit[[1]]))
cat("\ninfocriteria method:\n")
infocriteria(fit[[1]])
cat("\nlikelihood method:\n")
likelihood(fit[[1]])
cat("\nresiduals method:\n")
# Note that we the package will always return the full length residuals and
# fitted object irrespective of the lags (i.e. since this is an ARMA(1,1)
# i.e. max lag = 1, the first row is zero and should be discarded).
head(residuals(fit[[1]]))
cat("\nuncmean method:\n")
uncmean(fit[[1]])
cat("\nuncmean method (by simulation):\n")
uncmean(fit[[1]], method = "simulation", n.sim = 100000, rseed = 100)
cat("\nsummary method:\n")
show(fit[[1]])
## End(Not run)