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.
Setting Seeds
To guarantee reproducibility, you can either use boottest()'s
seed
function argument, or
set a global random seed via
set.seed()
when usingthe lean algorithm (via
boot_algo = "R-lean"
), 2) the heteroskedastic wild bootstrapthe wild cluster bootstrap via
boot_algo = "R"
with Mammen weights or 4)boot_algo = "WildBootTests.jl"
dqrng::dqset.seed()
when usingboot_algo = "R"
for Rademacher, Webb or Normal weights
References
Roodman et al., 2019, "Fast and wild: Bootstrap inference in STATA using boottest", The STATA Journal. (https://journals.sagepub.com/doi/full/10.1177/1536867X19830877)
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.
MacKinnon, James G., and Matthew D. Webb. "The wild bootstrap for few (treated) clusters." The Econometrics Journal 21.2 (2018): 114-135.
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.