sigma.summation: Computes the asymptotic variance estimator

In conroylau/lpinfer: An R Package for Inference in Linear Programs

Computes the asymptotic variance estimator

Description

Based on the bootstrap estimates \{\widehat{\bm{\beta}}_b\}^B_{b=1}, this function computes the asymptotic variance estimator of the bootstrap estimator, i.e.

\frac{n}{B} \sum^B_{i=1} \left(\widehat{\bm{\beta}}_b - \widehat{\bm{\beta}}\right) \left(\widehat{\bm{\beta}}_b - \widehat{\bm{\beta}}\right)'.

This function supports parallel programming via the furrr package.

Usage

sigma.summation(n, beta.bs.list, progress, eval.count)

Arguments

n	Sample size.
beta.bs.list	A list of bootstrap estimators \{\widehat{\bm{\beta}}_b\}^B_{b=1}.
progress	The boolean variable for whether the progress bars should be displayed. If it is set as TRUE, the progress bars will be displayed while the code is running.
eval.count	The count for the number of times the future_map function has been called. If this object is zero, it means that the future_map function is being called for the first time in this subprocedure. Otherwise, it means that the future_map function has been called for more than once. This situation typically refers to the situations where there are some errors in the first time of the replications.

Value

Returns the estimator of the asymptotic variance.

sigma.mat

The estimator of the asymptotic variance.

conroylau/lpinfer documentation built on Oct. 25, 2024, 4 a.m.