ARMAacf_cpp

Compute the theoretical autocorrelation function for an ARMA process.

internal

A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.

St<c3><a9>phane Guerrier

simts

Time Series Analysis Tools

Stéphane Guerrier

James Balamuta

Roberto Molinari

Justin Lee

Lionel Voirol

Yuming Zhang

Wenchao Yang

Nathanael Claussen

Yunxiang Zhang

Christian Gunning

Romain Francois

Ross Ihaka

R Core Team 

ARMAacf_cpp function

<dl><dt>ar</dt>
<dd>A <code>vector</code> of length p containing AR coefficients</dd>
<dt>ma</dt>
<dd>A <code>vector</code> of length q containing MA coefficients</dd>
<dt>lag_max</dt>
<dd>A <code>unsigned integer</code> indicating the maximum lag necessary</dd></dl>

Arguments

Author

Compute Theoretical ACF for an ARMA Process — ARMAacf_cpp

<dl>

<dt>ar</dt>
<dd>A <code>vector</code> of length p containing AR coefficients</dd>


<dt>ma</dt>
<dd>A <code>vector</code> of length q containing MA coefficients</dd>


<dt>lag_max</dt>
<dd>A <code>unsigned integer</code> indicating the maximum lag necessary</dd>

</dl>

Compute Theoretical ACF for an ARMA Process

ARMAacf_cpp: Compute Theoretical ACF for an ARMA Process

Description

Usage

Value

Arguments

Author

Details