Method for simulating and estimating the parameter distribution from an ARFIMA models as well as the simulation based consistency of the estimators given the data size.
arfimadistribution(fitORspec, n.sim = 2000, n.start = 1, m.sim = 100,
recursive = FALSE, recursive.length = 6000, recursive.window = 1000,
prereturns = NA, preresiduals = NA, rseed = NA,
custom.dist = list(name = NA, distfit = NA, type = "z"), mexsimdata = NULL,
fit.control = list(), solver = "solnp", solver.control = list(),
cluster = NULL, ...)A ARFIMAdistribution object containing details of the
ARFIMA simulated parameters distribution.
Either an ARFIMA fit object of class
ARFIMAfit or alternatively an ARFIMA specification object
of class ARFIMAspec with valid parameters supplied via the
fixed.pars argument in the specification.
The simulation horizon.
The burn-in sample.
The number of simulations.
Whether to perform a recursive simulation on an expanding window.
If recursive is TRUE, this indicates the final
length of the simulation horizon, with starting length n.sim.
If recursive is TRUE, this indicates the
increment to the expanding window. Together with recursive.length, it
determines the total number of separate and increasing length windows which will
be simulated and fitted.
Allows the starting return data to be provided by the user.
Allows the starting residuals to be provided by the user.
Optional seeding value(s) for the random number generator.
Optional density with fitted object from which to simulate.
Matrix of simulated external regressor-in-mean data. If the fit object contains external regressors in the mean equation, this must be provided.
One of either “nlminb” or “solnp”.
Control arguments list passed to optimizer.
Control arguments passed to the fitting routine (as in the
arfimafit method).
A cluster object created by calling makeCluster from the parallel
package. If it is not NULL, then this will be used for parallel estimation.
.
Alexios Ghalanos
This method facilitates the simulation and evaluation of the uncertainty of
ARFIMA model parameters. The recursive option also allows the evaluation of the
simulation based consistency (in terms of sqrt(N) ) of the parameters as the
length (n.sim) of the data increases, in the sense of the root mean square error
(rmse) of the difference between the simulated and true (hypothesized)
parameters.
This is an expensive function, particularly if using the recursive
option, both on memory and CPU resources, performing many re-fits of the
simulated data in order to generate the parameter distribution.