GetFitARpMLE: Exact MLE for subset ARp Models
In FitAR: Subset AR Model Fitting

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

Uses built-in function arima to fit subset ARp model, that is, the subset model is formed by constraining some coefficients to zero.

1	GetFitARpMLE(z, pvec)

`z`	time series
`pvec`	lags included in AR model. If pvec = 0, white noise model assumed.

Due to the optimization algorithms used by arima, this method is not very reliable. The optimization may simply fail. Example 1 shows it working but in Example 2 below it fails.

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.

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, RacfPlot

#Example 1. MLE works
z<-log(lynx)
p<-c(1,2,4,7,10,11)
GetFitARpMLE(z, p)
#
#Example 2. MLE fails with error.
p<-c(1,2,9,12)
## Not run: GetFitARpMLE(z, p)