General-to-Specific (GETS) Modelling of objects of class 'logitx'

General-to-Specific (GETS) Modelling of a dynamic Autoregressive (AR) logit model with covariates ('X') of class 'dlogitx'.

Usage

# S3 method for class 'logitx'
gets(x, t.pval = 0.05, wald.pval = t.pval, do.pet = TRUE, 
    user.diagnostics = NULL, keep = NULL, include.gum = FALSE,
    include.1cut = TRUE, include.empty = FALSE, max.paths = NULL,
    turbo = TRUE, print.searchinfo = TRUE, plot = NULL, alarm = FALSE,
    ...)

Arguments

x: an object of class 'logitx', see logitx
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
do.pet: logical that determines whether a Parsimonious Encompassing Test (PET) against the GUM should be 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
user.diagnostics: NULL (default) or a list with two entries, name and pval, see getsFun
keep: NULL or a vector of integers that determines which regressors to be excluded from removal in the specification search
include.gum: logical that determines whether the GUM (i.e. the starting model) should be included among the terminal models. If FALSE (default), then the GUM is not included
include.1cut: logical that determines whether the 1-cut model should be 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 that determines whether an empty model should be added to the list of terminal models, if it passes the diagnostic tests. If FALSE (default), then the empty model is not added, unless it is a terminal model in one of the paths
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)
turbo: logical. If TRUE (the default), then (parts of) paths are not searched twice (or more) unnecessarily, thus yielding a significant potential for speed-gain. The checking of whether the search has arrived at a point it has already been at comes with a slight computational overhead. So faster search is not guaranteed when turbo=TRUE
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 a plot is produced. 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 to or from other methods

Details

The model of class 'logitx' is a dynamic Autoregressive (AR) logit model with (optional) covariates ('X') proposed by Kauppi and Saikkonen (2008). Internally, gets.logitx undertakes the General-to-Specific (GETS) modelling with the getsFun function, see Sucarrat (2020).

References

Heikki Kauppi and Penti Saikkonen (2008): 'Predicting U.S. Recessions with Dynamic Binary Response Models'. The Review of Economic Statistics 90, pp. 777-791

Author

Genaro Sucarrat, http://www.sucarrat.net/

Examples


##simulate from ar(1), create covariates:
set.seed(123) #for reproducibility
y <- logitxSim(100, ar=0.3)
x <- matrix(rnorm(5*100), 100, 5)

##estimate model:
mymod <- logitx(y, ar=1:4, xreg=x)

##do gets modelling:
gets(mymod)
#> 10 path(s) to search
#> Searching: 
#> 1 
#> 2 
#> 3 
#> 4 
#> 5 
#> 6 
#> 7 
#> 8 
#> 9 
#> 10 
#> 
#> Date: Sat Jul 27 15:29:53 2024 
#> Dependent var.: y 
#> Method: Maximum Likelihood (logit) 
#> Variance-Covariance: Ordinary 
#> No. of observations: 96 
#> Sample: 5 to 100 
#> 
#> Start model (GUM):
#> 
#>           reg.no. keep       coef std.error    t-stat p-value  
#> intercept       1    0 -0.8500962   0.42943 -1.979613 0.02547 *
#> ar1             2    0  0.6026207   0.47106  1.279290 0.10212  
#> ar2             3    0  0.3959259   0.46187  0.857232 0.19685  
#> ar3             4    0  0.7065778   0.45945  1.537867 0.06388 .
#> ar4             5    0 -0.0086638   0.47389 -0.018282 0.49273  
#> xreg1           6    0 -0.3449343   0.24557 -1.404649 0.08186 .
#> xreg2           7    0 -0.1020382   0.24258 -0.420630 0.33754  
#> xreg3           8    0  0.0946418   0.22873  0.413766 0.34004  
#> xreg4           9    0 -0.0828681   0.22377 -0.370320 0.35603  
#> xreg5          10    0 -0.1679998   0.24140 -0.695929 0.24417  
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Paths searched: 
#> 
#> path 1 : 1 9 3 8 7 5 2 10 4 6 
#> path 2 : 2 5 7 9 8 10 3 1 4 6 
#> path 3 : 3 5 8 7 9 10 6 2 1 4 
#> path 4 : 4 9 5 8 7 10 3 1 2 6 
#> path 5 : 5 9 7 8 10 3 6 2 1 4 
#> path 6 : 6 9 7 5 8 10 3 2 1 4 
#> path 7 : 7 5 9 8 10 3 6 2 1 4 
#> path 8 : 8 5 7 9 10 3 6 2 1 4 
#> path 9 : 9 5 7 8 10 3 6 2 1 4 
#> path 10 : 10 5 9 7 8 3 6 2 1 4 
#> 
#> Terminal models: 
#> 
#> spec 1 :  
#> 
#>                 info(sc)     logl  n  k
#> spec 1 (1-cut):   1.3863 -66.5421 96  0
#> 
#>    The empty model
#>                        
#> Log-lik.(n=96) -66.5421