gauge_covar calculates the asymptotic covariance between two FODRs
with different cut-off values s and t for a given iteration using a given set
of parameters (true or estimated).
gauge_covar(
ref_dist = c("normal"),
sign_level1,
sign_level2,
initial_est = c("robustified", "saturated", "iis"),
iteration,
parameters,
split
)gauge_covar returns a numeric value.
A character vector that specifies the reference distribution
against which observations are classified as outliers. "normal" refers
to the normal distribution.
A numeric value between 0 and 1 that determines the first cutoff in the reference distribution against which observations are judged as outliers or not.
A numeric value between 0 and 1 that determines the second cutoff in the reference distribution against which observations are judged as outliers or not.
A character vector that specifies the initial estimator
for the outlier detection algorithm. "robustified" means that the full
sample 2SLS is used as initial estimator. "saturated" splits the
sample into two parts and estimates a 2SLS on each subsample. The
coefficients of one subsample are used to calculate residuals and determine
outliers in the other subsample. "iis" applies impulse indicator
saturation (IIS) as implemented in ivisat.
An integer >= 0 or character "convergence"
representing the iteration for which the outliers are calculated. Uses the
fixed point value if set to "convergence".
A list created by generate_param or
estimate_param_null that stores the parameters (true or estimated).
NULL permitted if ref_dist == "normal".
A numeric value strictly between 0 and 1 that determines
in which proportions the sample will be split. Can be NULL if
initial_est == "robustified".
Initial estimator "iis" uses the asymptotic variances of
"robustified" 2SLS because there is no formal theory for the
multi-block search.