Fast wild cluster bootstrap inference for joint hypotheses for object of class fixest

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

Usage

# S3 method for fixest
mboottest(
  object,
  clustid,
  B,
  R,
  r = rep(0, nrow(R)),
  bootcluster = "max",
  fe = NULL,
  type = "rademacher",
  impose_null = TRUE,
  p_val_type = "two-tailed",
  tol = 1e-06,
  floattype = "Float64",
  getauxweights = FALSE,
  maxmatsize = NULL,
  bootstrapc = FALSE,
  ssc = boot_ssc(adj = TRUE, fixef.K = "none", cluster.adj = TRUE, cluster.df =
    "conventional"),
  ...
)

Arguments

object: An object of class feols
clustid: A character vector or rhs formula containing the names of the cluster variables
B: Integer. The number of bootstrap iterations. When the number of clusters is low, increasing B adds little additional runtime.
R: Hypothesis Vector or Matrix giving linear combinations of coefficients. Must be either a vector of length k or a matrix of dimension q x k, where q is the number of joint hypotheses and k the number of estimated coefficients.
r: A vector of length q, where q is the number of tested hypotheses. Shifts the null hypothesis H0: param = r vs H1: param != r. If not provided, a vector of zeros of length q.
bootcluster: A character vector or rhs formula of length 1. Specifies the bootstrap clustering variable or variables. If more than one variable is specified, then bootstrapping is clustered by the intersections of clustering implied by the listed variables. To mimic the behavior of stata's boottest command, the default is to cluster by the intersection of all the variables specified via the clustid argument, even though that is not necessarily recommended (see the paper by Roodman et al cited below, section 4.2). Other options include "min", where bootstrapping is clustered by the cluster variable with the fewest clusters. Further, the subcluster bootstrap (MacKinnon & Webb, 2018) is supported - see the vignette("fwildclusterboot", package = "fwildclusterboot") for details.
fe: A character vector or rhs formula of length one which contains the name of the fixed effect to be projected out in the bootstrap. Note: if regression weights are used, fe needs to be NULL.
type: character or function. The character string specifies the type of boostrap to use: One of "rademacher", "mammen", "norm", "gamma" and "webb". Alternatively, type can be a function(n) for drawing wild bootstrap factors. "rademacher" by default. For the Rademacher and Mammen distribution, if the number of replications B exceeds the number of possible draw ombinations, 2^(#number of clusters), then boottest() will use each possible combination once (enumeration).
impose_null: Logical. Controls if the null hypothesis is imposed on the bootstrap dgp or not. Null imposed (WCR) by default. If FALSE, the null is not imposed (WCU)
p_val_type: Character vector of length 1. Type of p-value. By default "two-tailed". Other options include "equal-tailed", ">" and "<".
tol: Numeric vector of length 1. The desired accuracy (convergence tolerance) used in the root finding procedure to find the confidence interval. Relative tolerance of 1e-6 by default.
floattype: Float64 by default. Other option: Float32. Should floating point numbers in Julia be represented as 32 or 64 bit?
getauxweights: Logical. FALSE by default. Whether to save auxilliary weight matrix (v)
maxmatsize: NULL by default = no limit. Else numeric scalar to set the maximum size of auxilliary weight matrix (v), in gigabytes
bootstrapc: Logical scalar, FALSE by default. TRUE to request bootstrap-c instead of bootstrap-t
ssc: An object of class boot_ssc.type obtained with the function boot_ssc(). Represents how the small sample adjustments are computed. The defaults are adj = TRUE, fixef.K = "none", cluster.adj = "TRUE", cluster.df = "conventional". You can find more details in the help file for boot_ssc(). The function is purposefully designed to mimic fixest's fixest::ssc() function.
...: Further arguments passed to or from other methods.

Value

An object of class mboottest

p_val: The bootstrap p-value.
N: Sample size. Might differ from the regression sample size if the cluster variables contain NA values.
boot_iter: Number of Bootstrap Iterations.
clustid: Names of the cluster Variables.
N_G: Dimension of the cluster variables as used in boottest.
sign_level: Significance level used in boottest.
type: Distribution of the bootstrap weights.
impose_null: Whether the null was imposed on the bootstrap dgp or not.
R: The vector "R" in the null hypothesis of interest Rbeta = r.
r: The scalar "r" in the null hypothesis of interest Rbeta = r.
point_estimate: R'beta. A scalar: the constraints vector times the regression coefficients.
teststat_stat: The 'original' regression test statistics.
teststat_boot: All bootstrap t-statistics.
regression: The regression object used in boottest.
call: Function call of boottest.

Setting Seeds

To guarantee reproducibility, you need to set a global random seed viaset.seed()

Multiple Fixed Effects

If your feols() model contains fixed effects, boottest() will internally convert all fixed effects but the one specified via the fe argument to dummy variables.

Run `boottest` quietly

You can suppress all warning and error messages by setting the following global options: options(rlib_warning_verbosity = "quiet") options(rlib_message_verbosity = "quiet") Not that this will turn off all warnings (messages) produced via rlang::warn() and rlang::inform(), which might not be desirable if you use other software build on rlang, as e.g. the tidyverse.

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) 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.

Examples

if (FALSE) {
requireNamespace("fwildclusterboot")
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)
}