fwildclusterboot (version 0.3.7)

boottest.felm: 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.

Usage

# S3 method for felm
boottest(
  object,
  param,
  B,
  clustid,
  bootcluster = "max",
  fe = NULL,
  conf_int = NULL,
  seed = NULL,
  beta0 = 0,
  sign_level = NULL,
  type = "rademacher",
  impose_null = TRUE,
  p_val_type = "two-tailed",
  tol = 1e-06,
  maxiter = 10,
  na_omit = TRUE,
  nthreads = getBoottest_nthreads(),
  ...
)

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.

seed

An integer. Allows the user to set a random seed. If NULL, boottest() sets an internal seed. Hence by default, calling boottest() multiple times on the same object will produce the same test statistics.

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" 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".

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.

maxiter

Integer. Maximum number of iterations used in the root finding procedure to find the confidence interval. 10 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).

nthreads

The number of threads. Can be: a) an integer lower than, or equal to, the maximum number of threads; b) 0: meaning all available threads will be used; c) a number strictly between 0 and 1 which represents the fraction of all threads to use. The default is to use 1 core.

...

Further arguments passed to or from other methods.

Value

An object of class boottest

p_val

The bootstrap p-value.

t_stat

The regression t-statistic of interest, recalculated in the bootstrap.

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.

test_vals

All t-statistics calculated while calculating the confidence interval.

regression

The regression object used in boottest.

call

Function call of boottest.

Confidence Intervals

boottest computes confidence intervals by inverting p-values. In practice, the following procedure is used:

• Based on an initial guess for starting values, calculate p-values for 26 equal spaced points between the starting values.

• Out of the 26 calculated p-values, find the two pairs of values x for which the corresponding p-values px cross the significance level sign_level.

• Feed the two pairs of x into an numerical root finding procedure and solve for the root. boottest currently relies on stats::uniroot and sets an absolute tolerance of 1e-06 and stops the procedure after 10 iterations.

Standard Errors

boottest does not calculate standard errors.

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)

Examples

# NOT RUN {
if(requireNamespace("lfe")){
library(fwildclusterboot)
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, 
                  seed = 8,
                  beta0 = 2)
summary(boot1)
plot(boot1)
}
# }