Package 'wildwyoung'

Summary method for objects of type wyoung

Description

Summary method for objects of type wyoung

Usage

## S3 method for class 'wyoung'
summary(object, digits, ...)

Arguments

object	An object of type wyoung
digits	Rounding of digits
...	misc. function arguments

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Westfall-Young multiple hypotheses adjusted p-values

Description

Function implements the Westfall-Young multiple hypthesis correction procedure for objects of type fixest_multi (fixest_multi are objects created by fixest::feols() that use feols() multiple-estimation interface).

Usage

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

Arguments

models	An object of type fixest_multi or a list of objects of type fixest
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", ">" and "<".
weights_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 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).
seed	Integer. Sets the random seed. NULL by default.
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'.
nthreads	Integer. The number of threads to use.
bootstrap_type	Either "11", "13", "31", "33", or "fnw11".
...	additional function values passed to the bootstrap function.

Value

An object of type wyoung

References

Westfall, Peter H., and S. Stanley Young. Resampling-based multiple testing: Examples and methods for p-value adjustment. Vol. 279. John Wiley & Sons, 1993.

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

Examples

library(fixest)
library(wildwyoung)

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_wyoung <- wyoung(models = res, param = "X1", B = B)
summary(res_wyoung)

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