GetLeapsAR {FitAR} | R Documentation |
The subset ARp model is the usual subset model, for example see Tong (1977). This function is used by SelectModel for model identification for ARp models.
GetLeapsAR(z, lag.max = 15, Criterion = "UBIC", Best = 3, Candidates=5)
z |
ts object or vector containing time series |
lag.max |
maximum order of the AR |
Criterion |
default UBIC, other choices are "AIC" or "BIC" |
Best |
the number of based selected based on highest AIC/BIC |
Candidates |
number of models initially selected using the approximate criterion |
The R function leaps in the R package leaps is used to compute the subset regression model with the smallest residual sum of squares containing 1, ..., lag.max parameters. The mean is always included, so the only parameters considered are the phi coefficients. After the best models containing 1, ..., lag.max parameters are selected the models are individually refit to determine the exact likelihood function for each selected model. Based on this likelihood the UBIC/BIC/AIC is computed and then the best models are selected. The UBIC criterion was developed by Chen and Chen (2007).
a list with components
NumParameters |
|
UBIC |
|
AIC |
|
BIC |
|
p |
{lags present}
AIC and BIC values produced are not comparable to AIC and BIC produced by SelectModel for ARz models. However comparable AIC/BIC values are produced when the selected models are fit by FitAR.
Requires leaps package
A.I. McLeod
Tong, H. (1977) Some comments on the Canadian lynx data. Journal of the Royal Statistical Society A 140, 432-436.
Chen, J. and Chen, Z. (2007). Extended Bayesian Information Criteria for Model Selection with Large Model Space. Preprint.
SelectModel
,
GetFitARpLS
,
leaps
#for the log(lynx) Tong (1977) selected an ARp(1,2,4,10,11) #using the AIC and a subset selection algorithm. Our more exact #approach shows that the ARp(1,2,3,4,10,11) has slightly lower #AIC (using exact likelihood evaluation). z<-log(lynx) GetLeapsAR(z, lag.max=11) GetLeapsAR(z, lag.max=11, Criterion="BIC")