wildrwolf (version 0.6.1)

rwolf: Romano-Wolf multiple hypotheses adjusted p-values

Description

Function implements the Romano-Wolf multiple hypothesis correction procedure for objects of type `fixest_multi` (`fixest_multi` are objects created by `fixest::feols()` that use `feols()` multiple-estimation interface). The null hypothesis is always imposed on the bootstrap dgp.

Usage

rwolf(
  models,
  param,
  B,
  R = NULL,
  r = 0,
  p_val_type = "two-tailed",
  weights_type = "rademacher",
  engine = "R",
  nthreads = 1,
  bootstrap_type = "fnw11",
  ...
)

Value

A data.frame containing the following columns:

model

Index of Models

Estimate

The estimated coefficient of `param` in the respective model.

Std. Error

The estimated standard error of `param` in the respective model.

t value

The t statistic of `param` in the respective model.

Pr(>|t|)

The uncorrected pvalue for `param` in the respective model.

RW Pr(>|t|)

The Romano-Wolf corrected pvalue of hypothesis test for `param` in the respective model.

Arguments

models

An object of type `fixest_multi` or a list of objects of type `fixest`, estimated via ordinary least squares (OLS)

param

The regression parameter to be tested

B

The number of bootstrap iterations

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.

r

A numeric. Shifts the null hypothesis H0: `param.` = r vs H1: `param.` != r

p_val_type

Character vector of length 1. Type of hypothesis test By default "two-tailed". Other options include "equal-tailed" (for one-sided tests), ">" and "<" (for two-sided tests).

weights_type

character or function. The character string specifies the type of bootstrap 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 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).

engine

Should the wild cluster bootstrap run via `fwildclusterboot's` R implementation or via `WildBootTests.jl`? 'R' by default. The other option is `WildBootTests.jl`. Running the bootstrap through `WildBootTests.jl` might significantly reduce the runtime of `rwolf()` for complex problems (e.g. problems with more than 500 clusters).

nthreads

Integer. The number of threads to use when running the bootstrap.

bootstrap_type

Either "11", "13", "31", "33", or "fnw11". "fnw11" by default. See `?fwildclusterboot::boottest` for more details

...

additional function values passed to the bootstrap function.

Setting Seeds and Random Number Generation

To guarantee reproducibility, please set a global random seeds via `set.seed()`.

References

Clarke, Romano & Wolf (2019), STATA Journal. IZA working paper: https://ftp.iza.org/dp12845.pdf

Examples

 
library(fixest)
library(wildrwolf)

set.seed(12345)

N <- 1000
X1 <- rnorm(N)
Y1 <- 1 + 1 * X1 + rnorm(N)
Y2 <- 1 + 0.01 * X1 + rnorm(N)
Y3 <- 1 + 0.01 * X1 + rnorm(N)
Y4 <- 1 + 0.01 * X1 + rnorm(N)

B <- 999
# intra-cluster correlation of 0 for all clusters
cluster <- rep(1:50, N / 50)

data <- data.frame(Y1 = Y1, 
                   Y2 = Y2, 
                   Y3 = Y3, 
                   Y4 = Y4,
                   X1 = X1, 
                   cluster = cluster)

res <- feols(c(Y1, Y2, Y3) ~ X1, data = data, cluster = ~ cluster)
res_rwolf <- rwolf(models = res, param = "X1", B = B)
res_rwolf