Impulse Indicator Saturation (IIS initial estimator)
iis_init(
data,
formula,
gamma,
t.pval = gamma,
do.pet = FALSE,
normality.JarqueB = NULL,
turbo = FALSE,
overid = NULL,
weak = NULL
)iis_init returns a list with five elements. The first
four are vectors whose length equals the number of observations in the data
set. Unlike the residuals stored in a model object (usually accessible via
model$residuals), it does not ignore observations where any of y, x
or z are missing. It instead sets their values to NA.
The first element is a double vector containing the residuals for each
observation based on the model estimates. The second element contains the
standardised residuals, the third one a logical vector with TRUE if
the observation is judged as not outlying, FALSE if it is an outlier,
and NA if any of y, x, or z are missing. The fourth element of the
list is an integer vector with three values: 0 if the observations is judged
to be an outlier, 1 if not, and -1 if missing. The fifth and last element
stores the ivreg model object based on which the four
vectors were calculated.
A dataframe.
A formula in the format y ~ x1 + x2 | x1 + z2 where
y is the dependent variable, x1 are the exogenous regressors,
x2 the endogenous regressors, and z2 the outside instruments.
A numeric value between 0 and 1 representing the significance level used for two-sided significance t-test on the impulse indicators. Corresponds to the probability of falsely classifying an observation as an outlier.
A numeric value between 0 and 1 representing the significance level for the Parsimonious Encompassing Test (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 t.pval
NULL (the default) or a value between 0 and 1. In the latter case, a test for non-normality is conducted using a significance level equal to normality.JarqueB. If NULL, then no test for non-normality is conducted
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
NULL if no Sargan test of overidentifying restrictions
should be used as a diagnostic check for model selection or a numeric value
between 0 and 1. In the latter case, the test is conducted using this value
as the significance level.
NULL if no weak instrument F-test on the first stage
should be used as a diagnostic check for model selection or a numeric value
between 0 and 1. In the latter case, the test is conducted using this value
as the significance level.