boottest: Fast wild cluster bootstrap inference

In fwildclusterboot: Fast Wild Cluster Bootstrap Inference for Linear Models

Fast wild cluster bootstrap inference

Description

boottest is a S3 method that allows for fast wild cluster bootstrap inference for objects of class lm, fixest and felm by implementing the fast wild bootstrap algorithm developed in Roodman et al., 2019.

Usage

boottest(object, ...)

Arguments

object	An object of type lm, fixest, felm or ivreg
...	other arguments

Value

An object of class boottest.

Setting Seeds

To guarantee reproducibility, you can either use boottest()'s seed function argument, or set a global random seed via

• set.seed() when using

  1. the lean algorithm (via engine = "R-lean"), 2) the heteroskedastic wild bootstrap

  2. the wild cluster bootstrap via engine = "R" with Mammen weights or 4) engine = "WildBootTests.jl"

• dqrng::dqset.seed() when using engine = "R" for Rademacher, Webb or Normal weights

Stata, Julia and Python Implementations

The fast wild cluster bootstrap algorithms are further implemented in the following software packages:

• Stata:boottest

• Julia:WildBootTests.jl

• Python:wildboottest

References

Roodman et al., 2019, "Fast and wild: Bootstrap inference in STATA using boottest", The STATA Journal. (https://ideas.repec.org/p/qed/wpaper/1406.html)

MacKinnon, James G., Morten Ørregaard Nielsen, and Matthew D. Webb. Fast and reliable jackknife and bootstrap methods for cluster-robust inference. No. 1485. 2022.

Cameron, A. Colin, Jonah B. Gelbach, and Douglas L. Miller. "Bootstrap-based improvements for inference with clustered errors." The Review of Economics and Statistics 90.3 (2008): 414-427.

Cameron, A.Colin & Douglas L. Miller. "A practitioner's guide to cluster-robust inference" Journal of Human Resources (2015) doi: 10.3368/jhr.50.2.317

Davidson & MacKinnon. "Wild Bootstrap Tests for IV regression" Journal of Economics and Business Statistics (2010) doi: 10.1198/jbes.2009.07221

MacKinnon, James G., and Matthew D. Webb. "The wild bootstrap for few (treated) clusters." The Econometrics Journal 21.2 (2018): 114-135.

MacKinnon, James G., and Matthew D. Webb. "Cluster-robust inference: A guide to empirical practice" Journal of Econometrics (2022) doi: 10.1016/j.jeconom.2022.04.001

MacKinnon, James. "Wild cluster bootstrap confidence intervals." L'Actualite economique 91.1-2 (2015): 11-33.

Webb, Matthew D. "Reworking wild bootstrap based inference for clustered errors" . No. 1315. Queen's Economics Department Working Paper, 2013.

See Also

boottest.lm, boottest.fixest, boottest.felm, boottest.ivreg

Examples

requireNamespace("fwildclusterboot")
data(voters)
lm_fit <- lm(
  proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration,
  data = voters
)
boot <- boottest(lm_fit,
  B = 9999,
  param = "treatment",
  clustid = "group_id1"
)
summary(boot)
print(boot)
plot(boot)
nobs(boot)
pval(boot)
confint(boot)
generics::tidy(boot)

fwildclusterboot documentation built on March 7, 2023, 5:33 p.m.