View source: R/getsbasesource.R
isat  R Documentation 
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.
isat(y, ...)
##default S3 method:
## Default S3 method:
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, info.method=c("sc","aic","hq"),
user.diagnostics=NULL, user.estimator=NULL, gof.function=NULL,
gof.method = c("min", "max"), include.gum=NULL, include.1cut=FALSE,
include.empty=FALSE, max.paths=NULL, parallel.options=NULL, turbo=FALSE,
tol=1e07, LAPACK=FALSE, max.regs=NULL, print.searchinfo=TRUE, plot=NULL,
alarm=FALSE, ...)
##S3 method for objects of class 'lm':
## S3 method for class 'lm'
isat(y, ar=NULL, ewma=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, info.method=c("sc","aic","hq"),
user.diagnostics=NULL, user.estimator=NULL, gof.function=NULL,
gof.method = c("min", "max"), include.gum=NULL, include.1cut=FALSE,
include.empty=FALSE, max.paths=NULL, parallel.options=NULL, turbo=FALSE,
tol=1e07, LAPACK=FALSE, max.regs=NULL, print.searchinfo=TRUE, plot=NULL,
alarm=FALSE, ...)
##S3 method for objects of class 'arx':
## S3 method for class 'arx'
isat(y, mc=TRUE, ar=NULL, ewma=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, info.method=c("sc","aic","hq"),
user.diagnostics=NULL, user.estimator=NULL, gof.function=NULL,
gof.method = c("min", "max"), include.gum=NULL, include.1cut=FALSE,
include.empty=FALSE, max.paths=NULL, parallel.options=NULL, turbo=FALSE,
tol=1e07, LAPACK=FALSE, max.regs=NULL, print.searchinfo=TRUE, plot=NULL,
alarm=FALSE, ...)
y 
numeric vector, timeseries, 
mc 
logical. 
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 
sis 
logical. If 
tis 
logical. If 
uis 
a matrix of regressors, or a list of matrices. 
blocks 

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 
ar.LjungB 
a twoitem list with names 
arch.LjungB 
a twoitem list with names 
normality.JarqueB 

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 
user.diagnostics 

user.estimator 

gof.function 

gof.method 

include.gum 
ignored (temporarily deprecated) 
include.1cut 
logical. If 
include.empty 
logical. If 
max.paths 

parallel.options 

turbo 
logical. If 
tol 
numeric value (default = 1e07). The tolerance for detecting linear dependencies in the columns of the regressors (see 
LAPACK 
logical. If 
max.regs 
integer. The maximum number of regressions along a deletion path. It is not recommended that this is altered 
print.searchinfo 
logical. If 
plot 
NULL or logical. If 
alarm 
logical. If 
... 
further arguments passed to or from other methods 
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 'isat'
Jonas Kurle, https://www.jonaskurle.com/
Felix Pretis, https://felixpretis.climateeconometrics.org/
James Reade, https://sites.google.com/site/jjamesreade/
Moritz Schwarz, https://www.inet.ox.ac.uk/people/moritzschwarz
Genaro Sucarrat https://www.sucarrat.net/
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.
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.
Pretis, Felix, Reade, James and Sucarrat, Genaro (2018): 'Automated GeneraltoSpecific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks'. Journal of Statistical Software 86, Number 3, pp. 144
Extraction functions for 'isat' objects: coef.isat
, fitted.isat
, paths
, plot.isat
, print.isat
,
residuals.isat
, summary.isat
, terminals
, vcov.isat
Related functions: arx
, eqwma
, leqwma
, zoo
, getsFun
##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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