armasubsets: Selection of Subset ARMA Models

In TSA: Time Series Analysis

Selection of Subset ARMA Models

Description

This function finds a number of subset ARMA models. A "long" AR model is fitted to the data y to compute the residuals which are taken as a proxy of the error process. Then, an ARMA model is approximated by a regression model with the the covariates being the lags of the time series and the lags of the error process. Subset ARMA models may then be selected using the subset regression technique by leaps and bounds, via the regsubsets function of the leaps package in R.

Usage

armasubsets(y, nar, nma, y.name = "Y", ar.method = "ols", ...)

Arguments

y	time-series data
nar	maximum AR order
nma	maximum MA order
y.name	label of the time series
ar.method	method used for fitting the long AR model; default is ols with the AR order determined by AIC
...	arguments passed to the plot.armasubsets function

Value

An object of the armasubsets class to be processed by the plot.armasubsets function.

Author(s)

Kung-Sik Chan

Examples

set.seed(92397)
test=arima.sim(model=list(ar=c(rep(0,11),.8),ma=c(rep(0,11),0.7)),n=120)
res=armasubsets(y=test,nar=14,nma=14,y.name='test',ar.method='ols')
plot(res)

TSA documentation built on July 5, 2022, 5:05 p.m.