arfima(x, drange=c(0, 0.5), estim=c("mle","ls"), lambda=NULL, ...)
c(0,0.5)
ensures a stationary model is returned.estim=="ls"
, then the ARMA parameters are calculated using the Haslett-Raftery algorithm. If estim=="mle"
, then the ARMA parameters are calculated using full MLE via the arima
auto.arima
when selecting p and q."fracdiff"
, which is described in the fracdiff
documentation. A few additional objects are added to the list including x
(the original time series),
and the residuals
and fitted
values.fracdiff
and auto.arima
to
automatically select and estimate an ARFIMA model. The fractional differencing parameter is chosen
first assuming an ARFIMA(2,d,0) model. Then the data are fractionally differenced using
the estimated d and an ARMA model is selected for the resulting time series using
auto.arima
. Finally, the full ARFIMA(p,d,q) model is re-estimated using
fracdiff
. If estim=="mle"
, the ARMA coefficients are refined using
arima
.fracdiff
, auto.arima
, forecast.fracdiff
.x <- fracdiff.sim( 100, ma=-.4, d=.3)$series
fit <- arfima(x)
tsdisplay(residuals(fit))
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