fracdiff( x, nar = 0, nma = 0, dtol = <see below>, M = 100)
x
| time series for the ARIMA model |
nar
| number of autoregressive parameters |
nma
| number of moving average parameters |
dtol
| interval of uncertainty for d If dtol is less than zero, the fourth root of machine precision will be used. dtol will be altered if necessary by the program. |
M
| number of terms in the likelihood approximation (see Haslett and Raftery 1989) |
log.likelihood
| logarithm of the maximum likelihood |
d
| optimal fractional-differencing parameter |
ar
| vector of optimal autoregressive parameters |
ma
| vector of optimal moving average parameters |
covariance.dpq
| covarianvce matrix of the parameter estimates (order : d, ar, ma) |
stderror.dpq
| standard errors of the parameter estimates (order : d, ar, ma) |
correlation.dpq
| correlation matrix of the parameter estimates (order : d, ar, ma) |
dtol
| interval of uncertainty for d |
lm
DER).
written by Chris Fraley (March 1991)fracdiff.sim
is available for generating test problems.R. Brent, Algorithms for Minimization without Derivatives, Prentice-Hall (1973).
J. J. More, B. S. Garbow, and K. E. Hillstrom, Users Guide for MINPACK-1, Technical Report ANL-80-74, Applied Mathematics Division, Argonne National Laboratory (August 1980).
fracdiff.sim
ts.test <- fracdiff.sim( 5000, ar = .2, ma = -.4, d = .3) fracdiff( ts.test$series, nar = length(ts.test$ar), nma = length(ts.test$ma))