p_val_null2

Scalar. Shifts the null hypothesis.

beta0

A data.frame containing the cluster variables.

clustid

An integer. Number of bootstrap iterations.

boot_iter

A vector of the dimension of ncol(clustid)

small_sample_correction

If TRUE, no null hypothesis if imposed on the bootstrap

impose_null

The point estimate of the test parameter from the regression model.

point_estimate

type Type of p-value. By default "two-tailed". Other options: "equal-tailed", "&gt;", "&lt;"

p_val_type

Calculate p-values based on A, B, CC, CD, DD and other inputs

Implementation of the fast algorithm for wild cluster bootstrap
inference developed in Roodman et al (2019, STATA Journal) for
linear regression models
<https://journals.sagepub.com/doi/full/10.1177/1536867X19830877>,
which makes it feasible to quickly calculate bootstrap test
statistics based on a large number of bootstrap draws even for
large samples - as long as the number of bootstrapping clusters
is not too large. Multiway clustering, regression weights,
bootstrap weights, fixed effects and subcluster bootstrapping
are supported. Further, both restricted (WCR) and unrestricted
(WCU) bootstrap are supported. Methods are provided for a variety
of fitted models, including 'lm()', 'feols()'
(from package 'fixest') and 'felm()' (from package 'lfe').

p_val_null2: Calculate p-values based on A, B, CC, CD, DD and other inputs

Description

Usage

Arguments

Value