arfimafit-methods: function: ARFIMA Fit
In rugarch: Univariate GARCH Models

arfimafit-methods

R Documentation

function: ARFIMA Fit

Description

Method for fitting an ARFIMA models.

Usage

arfimafit(spec, data, out.sample = 0, solver = "solnp", solver.control = list(), 
fit.control = list(fixed.se = 0, scale = 0), numderiv.control = list(grad.eps=1e-4, 
grad.d=0.0001, grad.zero.tol=sqrt(.Machine$double.eps/7e-7), hess.eps=1e-4, hess.d=0.1, 
hess.zero.tol=sqrt(.Machine$double.eps/7e-7), r=4, v=2), ...)

Arguments

`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 `ARFIMAspec`.
`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 `arfimaspec function). The scale parameter controls whether the data should be scaled before being submitted to the optimizer.`
`numderiv.control`	Control arguments passed to the numerical routines for the calculation of the standard errors. See the documentation in the numDeriv package for further details. The arguments which start with ‘hess’ are passed to the hessian routine while those with ‘grad’ to the jacobian routine.
`...`	.

Details

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” (logical), “pkg” (either snowfall or multicore) and “cores” (the number of cores or workers to use) for use of 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.