iis_init {robust2sls}

Impulse Indicator Saturation (IIS initial estimator)

Description

Impulse Indicator Saturation (IIS initial estimator)

Usage

iis_init(
  data,
  formula,
  gamma,
  t.pval = gamma,
  do.pet = FALSE,
  normality.JarqueB = NULL,
  turbo = FALSE,
  overid = NULL,
  weak = NULL
)

Arguments

data

A dataframe.

formula

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.

gamma

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.

t.pval

A numeric value between 0 and 1 representing the significance level for the Parsimonious Encompassing Test (PET).

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 t.pval

normality.JarqueB

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

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

overid

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.

weak

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.

Value

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.

Note

IIS runs multiple models, similar to saturated_init but with multiple block search. These intermediate models are not recorded. For simplicity, the element $model of the returned list stores the full sample model result, identical to robustified_init.