Description Usage Arguments Details Value Author(s) References See Also Examples
The starting model, an object of the 'arx' class, is referred to as the General Unrestricted Model (GUM). The getsm
function undertakes multipath GETS modelling of the mean specification, whereas getsv
does the same for the logvariance specification. The diagnostic tests are undertaken on the standardised residuals, and the keep
option enables regressors to be excluded from possible removal.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  ##gets of mean specification:
getsm(object, t.pval=0.05, wald.pval=t.pval, vcov.type=NULL, do.pet=TRUE,
ar.LjungB=list(lag=NULL, pval=0.025), arch.LjungB=list(lag=NULL, pval=0.025),
normality.JarqueB=NULL, user.diagnostics=NULL,
info.method=c("sc","aic","hq"), keep=NULL, include.gum=FALSE,
include.empty=FALSE, max.paths=NULL, max.regs=NULL, zero.adj=NULL,
vc.adj=NULL, verbose=TRUE, print.searchinfo=TRUE, estimate.specific=TRUE,
plot=NULL, alarm=FALSE)
##gets of variance specification:
getsv(object, t.pval=0.05, wald.pval=t.pval, do.pet=TRUE,
ar.LjungB=list(lag=NULL, pval=0.025), arch.LjungB=list(lag=NULL, pval=0.025),
normality.JarqueB=NULL, user.diagnostics=NULL,
info.method=c("sc","aic","hq"), keep=c(1), include.gum=FALSE,
include.1cut=FALSE, include.empty=FALSE, max.paths=NULL, turbo=FALSE,
max.regs=NULL, zero.adj=NULL, vc.adj=NULL, print.searchinfo=TRUE,
estimate.specific=TRUE, plot=NULL, alarm=FALSE)

object 
an object of class 'arx' 
t.pval 
numeric value between 0 and 1. The significance level used for the twosided regressor significance ttests 
wald.pval 
numeric value between 0 and 1. The significance level used for the Parsimonious Encompassing Tests (PETs). By default, it is the same as 
vcov.type 
the type of variancecovariance matrix used. If 
do.pet 
logical. If 
ar.LjungB 
a twoitem list with names 
arch.LjungB 
a twoitem list with names 
normality.JarqueB 
a value between 0 and 1, or 
user.diagnostics 

info.method 
character string, "sc" (default), "aic" or "hq", which determines the information criterion to be used when selecting among terminal models. The abbreviations are short for the Schwarz or Bayesian information criterion (sc), the Akaike information criterion (aic) and the HannanQuinn (hq) information criterion 
keep 
the regressors to be excluded from removal in the specification search. Note that 
include.gum 
logical. If 
include.1cut 
logical. If 
include.empty 
logical. If 
max.paths 

turbo 
logical. If 
max.regs 
integer. The maximum number of regressions along a deletion path. It is not recommended that this is altered 
zero.adj 
numeric value between 0 and 1. The quantile adjustment for zero values. The default 0.1 means the zero residuals are replaced by the 10 percent quantile of the absolute residuals before taking the logarithm 
vc.adj 
logical. If 
verbose 
logical. 
print.searchinfo 
logical. If 
estimate.specific 
logical. IF 
plot 

alarm 
logical. If 
See Sucarrat and Escribano (2012)
A list of class 'gets'
Genaro Sucarrat, http://www.sucarrat.net/
Genaro Sucarrat and Alvaro Escribano (2012): 'Automated Financial Model Selection: GeneraltoSpecific Modelling of the Mean and Volatility Specifications', Oxford Bulletin of Economics and Statistics 74, Issue no. 5 (October), pp. 716735
C. Jarque and A. Bera (1980): 'Efficient Tests for Normality, Homoscedasticity and Serial Independence'. Economics Letters 6, pp. 255259
G. Ljung and G. Box (1979): 'On a Measure of Lack of Fit in Time Series Models'. Biometrika 66, pp. 265270
Extraction functions: coef.gets
, fitted.gets
, paths
, plot.gets
, print.gets
,
residuals.gets
, summary.gets
, terminals
, vcov.gets
Related functions: arx
, isat
, eqwma
, leqwma
, zoo
, getsFun
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34  ##Simulate from an AR(1):
set.seed(123)
y < arima.sim(list(ar=0.4), 80)
##Simulate four independent Gaussian regressors:
xregs < matrix(rnorm(2*80), 80, 2)
##estimate an AR(2) with intercept and four conditioning
##regressors in the mean, and a logARCH(3) with log(xregs^2) as
##regressors in the logvariance:
gum01 < arx(y, mc=TRUE, ar=1:2, mxreg=xregs, arch=1:3,
vxreg=log(xregs^2))
##GETS model selection of the mean:
meanmod01 < getsm(gum01)
##GETS model selection of the logvariance:
varmod01 < getsv(gum01)
##GETS model selection of the mean with the mean intercept
##excluded from removal:
meanmod02 < getsm(gum01, keep=1)
##GETS model selection of the mean with nondefault
#serialcorrelation diagnostics settings:
meanmod03 < getsm(gum01, ar.LjungB=list(pval=0.05))
##GETS model selection of the mean with very liberal
##(20 percent) significance levels:
meanmod04 < getsm(gum01, t.pval=0.2)
##GETS model selection of logvariance with all the
##logARCH terms excluded from removal:
varmod03 < getsv(gum01, keep=2:4)

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