mboottest: Arbitrary Linear Hypothesis Testing for Regression Models via...

In s3alfisc/fwildclusterboot: Fast Wild Cluster Bootstrap Inference for Linear Models

Arbitrary Linear Hypothesis Testing for Regression Models via Wald-Tests

Description

mboottest is a S3 method that allows for arbitrary linear hypothesis testing for objects of class lm, fixest, felm

Usage

mboottest(object, ...)

Arguments

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

Value

An object of class mboottest.

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

  3. 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

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)

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) \Sexpr[results=rd]{tools:::Rd_expr_doi("doi:10.3368/jhr.50.2.317")}

Davidson & MacKinnon. "Wild Bootstrap Tests for IV regression" Journal of Economics and Business Statistics (2010) \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/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) \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/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

mboottest.lm mboottest.felm mboottest.fixest

Examples

## Not run: 
requireNamespace("clubSandwich")
R <- clubSandwich::constrain_zero(2:3, coef(lm_fit))
wboottest <-
  mboottest(
    object = lm_fit,
    clustid = "group_id1",
    B = 999,
    R = R
  )
summary(wboottest)
print(wboottest)
nobs(wboottest)
pval(wboottest)
generics::tidy(wboottest)

## End(Not run)

s3alfisc/fwildclusterboot documentation built on Sept. 17, 2023, 5:55 a.m.