arfima0

The sample mean is asymptotically efficient.

Simulates, fits, and predicts long-memory and anti-persistent
time series, possibly mixed with ARMA, regression, transfer-function
components.
Exact methods (MLE, forecasting, simulation) are used.
Bug reports should be done via GitHub (at
<https://github.com/JQVeenstra/arfima>), where the development version
of this package lives; it can be installed using devtools.

JQ (Justin) Veenstra

arfima

Fractional ARIMA (and Other Long Memory) Time Series Modeling

arfima0 function

<dl><dt>z</dt>
<dd>time series</dd>
<dt>order</dt>
<dd>(p,d,q) where p=order AR, d=regular difference, q=order MA</dd>
<dt>lmodel</dt>
<dd>type of long-memory component: FD, FGN, PLA or NONE</dd></dl>

Arguments

JQ (Justin) Veenstra and A. I. McLeod

Author

Exact MLE for ARFIMA
 
The time series is corrected for the sample mean and then exact MLE is used
for the other parameters. This is a simplified version of the arfima()
function that may be useful in simulations and bootstrapping. — arfima0

<dl>

<dt>z</dt>
<dd>time series</dd>


<dt>order</dt>
<dd>(p,d,q) where p=order AR, d=regular difference, q=order MA</dd>


<dt>lmodel</dt>
<dd>type of long-memory component: FD, FGN, PLA or NONE</dd>

</dl>

Exact MLE for ARFIMA
 
The time series is corrected for the sample mean and then exact MLE is used
for the other parameters. This is a simplified version of the arfima()
function that may be useful in simulations and bootstrapping.

arfima0: Exact MLE for ARFIMA The time series is corrected for the sample mean and then exact MLE is used for the other parameters. This is a simplified version of the arfima() function that may be useful in simulations and bootstrapping.

Description

Usage

Value

Arguments

Author

Examples