Description Usage Arguments Details Value Author(s) References See Also Examples
The isat
function undertakes multipath indicator saturation to detect outliers and meanshifts using impulses (IIS), stepshifts (SIS), or trendindicators (TIS). Indicators are partitioned into blocks and selected over at a chosen level of significance (t.pval
) using the getsm
function.
1 2 3 4 5 6 7 8  isat(y, mc=TRUE, ar=NULL, ewma=NULL, mxreg=NULL, iis=FALSE, sis=TRUE,
tis=FALSE, uis=FALSE, blocks=NULL, ratio.threshold=0.8, max.block.size=30,
t.pval=0.001, wald.pval=t.pval,
vcov.type= c("ordinary","white","neweywest"), do.pet=FALSE, ar.LjungB=NULL,
arch.LjungB=NULL, normality.JarqueB=NULL, user.diagnostics=NULL,
info.method=c("sc","aic","hq"), include.gum=FALSE, include.empty=FALSE,
max.paths=NULL, parallel.options=NULL, tol=1e07, LAPACK=FALSE,
max.regs=NULL, print.searchinfo=TRUE, plot=NULL, alarm=FALSE)

y 
numeric vector, timeseries or 
mc 
logical. TRUE (default) includes an intercept in the mean specification, whereas FALSE does not 
ar 
integer vector, say, c(2,4) or 1:4. The ARlags to include in the mean specification 
ewma 
either NULL (default) or a list with arguments sent to the 
mxreg 
numeric vector or matrix, say, a 
iis 
logical. If TRUE, impulse indicator saturation is performed. 
sis 
logical. If TRUE, step indicator saturation is performed. 
tis 
logical. If TRUE, trend indicator saturation is performed. 
uis 
a matrix of regressors, or a list of matrices. 
blocks 
NULL (default), an integer (the number of blocks) or a userspecified 
ratio.threshold 
Minimum ratio of variables in each block to total observations to determine the block size, default=0.8. Only relevant if blocks = 
max.block.size 
Maximum size of block of variables to be selected over, default=30. Block size used is the maximum of given by either the ratio.threshold and max.block.size 
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) 
vcov.type 
the type of variancecovariance matrix used. If NULL (default), then the type used is that of the 'arx' object. This can be overridden by either "ordinary" (i.e. the ordinary variancecovariance matrix) or "white" (i.e. the White (1980) heteroscedasticity robust variancecovariance matrix) 
do.pet 
logical. If TRUE, then a Parsimonious Encompassing Test (PET) against the GUM is undertaken at each regressor removal for the joint significance of all the deleted regressors along the current path. If FALSE (default), then a PET is not undertaken at each regressor removal. By default, the numeric value is the same as that of 
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 
include.gum 
logical. If TRUE, then the GUM (i.e. the starting model) is included among the terminal models. If 
include.empty 
logical. If TRUE, then an empty model is included among the terminal models, if it passes the diagnostic tests, even if it is not equal to one of the terminals. If FALSE (default), then the empty model is not included (unless it is one of the terminals) 
max.paths 
currently not used 
parallel.options 

tol 
numeric value (default = 1e07). The tolerance for detecting linear dependencies in the columns of the regressors (see 
LAPACK 
logical. If TRUE, then use LAPACK. If FALSE (default), then use LINPACK (see 
max.regs 
integer. The maximum number of regressions along a deletion path. It is not recommended that this is altered 
print.searchinfo 
logical. If TRUE (default), then a print is returned whenever simiplification along a new path is started, and whenever regressors are dropped due to exact multicolinearity 
plot 
NULL or logical. If TRUE, then the fitted values and the residuals of the final model are plotted after model selection. If NULL (default), then the value set by 
alarm 
logical. If TRUE, then a sound is emitted (in order to alert the user) when the model selection ends 
Multipath indicator saturation using impulses (IIS), stepshifts (SIS), or trendindicators (TIS). Indicators are partitioned into sequential blocks (as of beta version 0.7) where the block intervals are defined by the ratio of variables to observations in each block and a specified maximum block size. Indicators are selected over using the getsm
function. Retained indicators in each block are combined and reselected over. Fixed covariates that are not selected over can be included in the regression model either in the mxreg matrix, or for autoregressive terms through the ar specification. See Hendry, Johansen and Santos (2007) and Castle, Doornik, Hendry, and Pretis (2015)
A list of class 'gets'
Felix Pretis, http://www.felixpretis.org/
James Reade, https://sites.google.com/site/jjamesreade/
Genaro Sucarrat, http://www.sucarrat.net/
Hendry, David, F., Johansen, Soren, and Santos, Carlos (2007): 'Automatic selection of indicators in a fully saturated regression'. Computational Statistics, vol 23:1, pp.317335.
Castle, Jennifer, L., Doornik, Jurgen, A., Hendry, David F., and Pretis, Felix (2015): 'Detecting Location Shifts during Model Selection by StepIndicator Saturation', Econometrics, vol 3:2, 240264.
Extraction functions for 'gets' objects: coef.gets
, fitted.gets
, paths
, plot.gets
, print.gets
,
residuals.gets
, summary.gets
, terminals
, vcov.gets
Related functions: arx
, 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  ##SIS using the Nile data
data(Nile)
isat(Nile, sis=TRUE, iis=FALSE, plot=TRUE, t.pval=0.005)
##SIS using the Nile data in an autoregressive model
#isat(Nile, ar=1:2, sis=TRUE, iis=FALSE, plot=TRUE, t.pval=0.005)
##HP Data
##load Hoover and Perez (1999) data:
#data(hpdata)
##make quarterly datamatrix of zoo type
##(GCQ = personal consumption expenditure):
#y < zooreg(hpdata$GCQ, 1959, frequency=4)
##transform data to logdifferences:
#dlogy < diff(log(y))
##run isat with step impulse saturation on four
##lags and a constant 1 percent significance level:
#isat(dlogy, ar=1:4, sis=TRUE, t.pval =0.01)
##Example with additional covariates entering through mxreg:
##(GYDQ = disposable personal income):
#x < zooreg(hpdata$GYDQ, 1959, frequency=4)
##transform data to logdifferences:
#dlogx < diff(log(x))
##run isat with step impulse saturation on four
##lags and a constant 1 percent significance level:
#isat(dlogy, mxreg=dlogx, ar=1:4, sis=TRUE, t.pval =0.01)

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