est.arma.wge: Function to calculate ML estimates of parameters of...

In tswge: Time Series for Data Science

Function to calculate ML estimates of parameters of stationary ARMA models

Description

This function calculates ML estimates, computes residuals (using backcasting), estimates white noise variance for a stationary ARMA model

Usage

est.arma.wge(x, p = 0, q = 0, factor = TRUE)

Arguments

x	The realization.
p	The autoregressive order
q	the moving average order
factor	Logical variable. factor=TRUE (default) plots a factor table for estimated AR-part of model

Details

This function uses arima from base SAS and is written similarly to itsmr function arma

Value

phi	ML estimates of autoregressive parameters
theta	ML estimates of moving average parameters
res	Residuals (calculated using backcasting)
avar	Estimate of white noise variance based on backcast residuals
se.phi	Standard errors of the AR parameter estimates
se.theta	Standard errors of the MA parameter estimates
aic	AIC for estimated model
aicc	AICC for estimated model
bic	BIC for estimated model

Note

Requires CRAN package 'itsmr'. The program is based on arima from base R and arma from 'itsmr'

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(fig6.2nf)
          est.arma.wge(fig6.2nf,p=2,q=1)

tswge documentation built on Feb. 16, 2023, 6:51 p.m.