boottest.felm: Fast wild cluster bootstrap inference for object of class...

In s3alfisc/wildboottestjlr: Fast Wild Cluster Bootstrap Inference in R via WildBootTests.jl and JuliaConnectoR

Fast wild cluster bootstrap inference for object of class felm

Description

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

Usage

## S3 method for class 'felm'
boottest(
  object,
  param,
  B,
  clustid,
  bootcluster = "max",
  fe = NULL,
  conf_int = NULL,
  rng = NULL,
  R = NULL,
  beta0 = 0,
  sign_level = 0.05,
  type = "rademacher",
  impose_null = TRUE,
  p_val_type = "two-tailed",
  tol = 1e-06,
  na_omit = TRUE,
  floattype = "Float32",
  fedfadj = TRUE,
  fweights = FALSE,
  getauxweights = FALSE,
  t_boot = FALSE,
  turbo = 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 felm
param	A character vector of length one. The name of the regression coefficient for which the hypothesis is to be tested
B	Integer. The number of bootstrap iterations. When the number of clusters is low, increasing B adds little additional runtime.
clustid	A character vector containing the names of the cluster variables
bootcluster	A character vector. 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.
fe	A character vector 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.
conf_int	A logical vector. If TRUE, boottest computes confidence intervals by p-value inversion. If FALSE, only the p-value is returned.
rng	An integer. Controls the random number generation, which is handled via the StableRNG() function from the StableRNGs Julia package.
R	Hypothesis Vector giving linear combinations of coefficients. Must be either NULL or a vector of the same length as param. If NULL, a vector of ones of length param.
beta0	A numeric. Shifts the null hypothesis H0: param = beta0 vs H1: param != beta0
sign_level	A numeric between 0 and 1 which sets the significance level of the inference procedure. E.g. sign_level = 0.05 returns 0.95% confidence intervals. By default, sign_level = 0.05.
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. 1e-6 by default.
na_omit	Logical. If TRUE, boottest() omits rows with missing variables in the cluster variable that have not previously been deleted when fitting the regression object (e.g. if the cluster variable was not used when fitting the regression model).
floattype	Float32 by default. Other optio: Float64. Should floating point numbers in Julia be represented as 32 or 64 bit?
fedfadj	Logical. TRUE by default. Should the small-sample adjustment reflect number of fixed effects?
fweights	Logical. FALSE by default, TRUE for frequency weights.
getauxweights	Logical. FALSE by default. Whether to save auxilliary weight matrix (v)
t_boot	Logical. Should bootstrapped t-statistics be returned?
turbo	Logical scalar, FALSE by default. Whether to exploit acceleration of the LoopVectorization package: slower on first use in a session, faster after
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 ssc function. #' @param ... Further arguments passed to or from other methods.
...	Further arguments passed to or from other methods.

Value

An object of class boottest

p_val	The bootstrap p-value.
conf_int	The bootstrap confidence interval.
param	The tested parameter.
N	Sample size. Might differ from the regression sample size if the cluster variables contain NA values.
B	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.
p_test_vals	All p-values calculated while calculating the confidence interval.
t_stat	The original test statistics - either imposing the null or not - with small sample correction G / (G-1).
test_vals	All t-statistics calculated while calculating the confidence interval.
t_boot	All bootstrap t-statistics.
regression	The regression object used in boottest.
call	Function call of boottest.
getauxweights	The bootstrap auxiliary weights matrix v. Only returned if getauxweights = TRUE.
t_boot	The bootstrapped t-statistics. Only returned if t_boot = TRUE.

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.

Examples

## Not run: 
    library(wildboottestjlr)
   library(lfe)
   data(voters)
   felm_fit <- felm(proposition_vote ~ treatment + ideology1 + log_income
              | Q1_immigration,
              data = voters)
   boot1 <- boottest(felm_fit,
                     B = 9999,
                     param = "treatment",
                     clustid = "group_id1")
   boot2 <- boottest(felm_fit,
                     B = 9999,
                     param = "treatment",
                     clustid = c("group_id1", "group_id2"))
   boot3 <- boottest(felm_fit,
                     B = 9999,
                     param = "treatment",
                     clustid = c("group_id1", "group_id2"),
                     fe = "Q1_immigration")
   boot4 <- boottest(felm_fit,
                     B = 999,
                     param = "treatment",
                     clustid = c("group_id1", "group_id2"),
                     fe = "Q1_immigration",
                     sign_level = 0.2,
                     rng = 8,
                     beta0 = 2)
   # test treatment + ideology1 = 2
   boot5 <- boottest(felm_fit,
                     B = 9999,
                     clustid = c("group_id1", "group_id2"),
                     param = c("treatment", "ideology1"),
                     R = c(1, 1),
                     beta0 = 2)
   summary(boot1)
   #plot(boot1)

## End(Not run)

s3alfisc/wildboottestjlr documentation built on June 16, 2022, 7:40 a.m.