create_data

A scalar. number of clusters for clustering variable 1

N_G1

A scalar between 0 and 1. intra-cluster correlation for clustering variable 1

icc1

A scalar. number of clusters for clustering variable 2

N_G2

A scalar between 0 and 1. intra-cluster correlation for clustering variable 2

icc2

A scalar. Number of fixed effect for first factor variable

numb_fe1

A scalar. Number of fixed effect for second factor variable

numb_fe2

seed

Possible regression weights to be used in estimation

weights

Function simulates data for tests and examples with clustering variables
and fixed-effects.

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').

create_data: Simulate Data

Description

Usage

Arguments

Value