boottest | R Documentation |
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.
boottest(object, ...)
object |
An object of type lm, fixest, felm or ivreg |
... |
other arguments |
An object of class boottest
.
To guarantee reproducibility, you can either use boottest()'s
seed
function argument, or
set a global random seed via
set.seed()
when using
the lean algorithm (via engine = "R-lean"
), 2) the heteroskedastic
wild bootstrap
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
The fast wild cluster bootstrap algorithms are further implemented in the following software packages:
Stata:boottest
Julia:WildBootTests.jl
Python:wildboottest
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.
boottest.lm, boottest.fixest, boottest.felm, boottest.ivreg
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)
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