GetFitARpLS: LS for AR(p) and Subset ARp - Short Version
In FitAR: Subset AR Model Fitting

Description Usage Arguments Details Value Note Author(s) References See Also Examples

For ARp subset models, the least squares estimates are computed. The exact loglikelihood is then determined. The estimated parameters are checked to see if they are in the AR admissible region.

1	GetFitARpLS(z, pvec)

`z`	vector or ts object, the time series
`pvec`	lags included in subset AR. If pvec = 0, white noise assumed.

The R function lsfit is used.

a list with components:

`loglikeliihood`	the exact loglikelihood
`phiHat`	estimated AR parameters
`constantTerm`	constant term in the linear regression
`pvec`	lags of estimated AR coefficient
`res`	the least squares regression residuals
`InvertibleQ`	True, if the estimated parameters are in the AR admissible region.
`yX`	the y vector and X matrix used for the regression fitting

This is a helper function for FitARp which is invoked by the main package function FitAR. Normally the user would FitAR since this function provides generic print, summary, resid and plot methods but GetFitARpLS is sometimes useful in iterative computations like bootstrapping since it is faster.

A.I. McLeod

McLeod, A.I. and Zhang, Y. (2006). Partial autocorrelation parameterization for subset autoregression. Journal of Time Series Analysis, 27, 599-612.

McLeod, A.I. and Zhang, Y. (2008a). Faster ARMA Maximum Likelihood Estimation, Computational Statistics and Data Analysis 52-4, 2166-2176. DOI link: http://dx.doi.org/10.1016/j.csda.2007.07.020.

McLeod, A.I. and Zhang, Y. (2008b, Submitted). Improved Subset Autoregression: With R Package. Journal of Statistical Software.

FitAR, FitARz, GetFitARz, FitARp, GetFitARpMLE, RacfPlot

#Fit subset AR using LS
#normally use FitAR
ans<-FitAR(SeriesA, c(1,2,7), ARModel="ARp", MLEQ=FALSE)
#could also use FitARp
ans<-FitARp(SeriesA, c(1,2,7))
#for some applications GetFitARpLS is simpler and faster
ansLS<-GetFitARpLS(SeriesA, c(1,2,7))
ansLS