gets.isat {gets}

General-to-Specific (GETS) Modelling 'isat' objects

Description

General-to-Specific (GETS) Modelling of a objects of class isat.

Usage

## S3 method for class 'isat'
gets(x, 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","aicc","hq"),
  gof.function=NULL, gof.method=NULL, keep=NULL, include.gum=FALSE,
  include.1cut=TRUE, include.empty=FALSE, max.paths=NULL, tol=1e-07,
  turbo=FALSE, print.searchinfo=TRUE, plot=NULL, alarm=FALSE,...) 

Arguments

x

an object of class 'isat'

t.pval

numeric value between 0 and 1. The significance level used for the two-sided regressor significance t-tests

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 t.pval

vcov.type

the type of variance-covariance matrix used. If NULL (default), then the type used in the estimation of the 'arx' object is used. This can be overridden by either "ordinary" (i.e. the ordinary variance-covariance matrix) or "white" (i.e. the White (1980) heteroscedasticity robust variance-covariance matrix)

do.pet

logical. If TRUE (default), 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, then a PET is not undertaken at each regressor removal

ar.LjungB

a two-item list with names lag and pval, or NULL. In the former case lag contains the order of the Ljung and Box (1979) test for serial correlation in the standardised residuals, and pval contains the significance level. If lag=NULL (default), then the order used is that of the estimated 'arx' object. If ar.Ljungb=NULL, then the standardised residuals are not checked for serial correlation

arch.LjungB

a two-item list with names lag and pval, or NULL. In the former case, lag contains the order of the Ljung and Box (1979) test for serial correlation in the squared standardised residuals, and pval contains the significance level. If lag=NULL (default), then the order used is that of the estimated 'arx' object. If arch.Ljungb=NULL, then the standardised residuals are not checked for ARCH

normality.JarqueB

a value between 0 and 1, or NULL. In the former case, the Jarque and Bera (1980) test for non-normality is conducted using a significance level equal to the numeric value. If NULL, then no test for non-normality is undertaken

user.diagnostics

NULL or a list with two entries, name and pval, see the user.fun argument in 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 Hannan-Quinn (hq) information criterion

gof.function

NULL (default) or a list, see getsFun. If NULL, then infocrit is used

gof.method

NULL (default) or a character, see getsFun. If NULL and gof.function is also NULL, then the best goodness-of-fit is characterised by a minimum value

keep

the regressors to be excluded from removal in the specification search. Note that keep=c(1) is obligatory when using getsv. This excludes the log-variance intercept from removal. The regressor numbering is contained in the reg.no column of the GUM

include.gum

logical. If TRUE, then the GUM (i.e. the starting model) is included among the terminal models. If FALSE (default), then the GUM is not included

include.1cut

logical. If TRUE, then the 1-cut model is added to the list of terminal models. If FALSE (default), then the 1-cut is not added, unless it is a terminal model in one of the paths

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

NULL (default) or an integer greater than 0. If NULL, then there is no limit to the number of paths. If an integer (e.g. 1), then this integer constitutes the maximum number of paths searched (e.g. a single path)

tol

numeric value. The tolerance for detecting linear dependencies in the columns of the variance-covariance matrix when computing the Wald-statistic used in the Parsimonious Encompassing Tests (PETs), see the qr.solve function

turbo

logical. If TRUE, then (parts of) paths are not searched twice (or more) unnecessarily, thus yielding a significant potential for speed-gain. However, the checking of whether the search has arrived at a point it has already been comes with a slight computational overhead. Accordingly, if turbo=TRUE, then the total search time might in fact be higher than if turbo=FALSE. This happens if estimation is very fast, say, less than quarter of a second. Hence the default is FALSE

print.searchinfo

logical. If TRUE (default), then a print is returned whenever simiplification along a new path is started

plot

NULL or logical. If TRUE, then the fitted values and the residuals of the final model are plotted after model selection. If FALSE, then they are not. If NULL (default), then the value set by options determines whether a plot is produced or not

alarm

logical. If TRUE, then a sound or beep is emitted (in order to alert the user) when the model selection ends

...

further arguments passed on to and from methods

Details

Internally, gets.isat invokes getsm for the GETS-modelling.

Value

A list of class gets.

Author(s)

Moritz Schwarz, https://www.inet.ox.ac.uk/people/moritz-schwarz
Genaro Sucarrat, https://www.sucarrat.net/

See Also

isat, getsm, getsFun, paths and terminals

Examples

##generate some data:
#set.seed(123) #for reproducibility
#y <- rnorm(30) #generate Y
#isatmod <- isat(y)
#gets(isatmot)