wild.boot: Cluster robust wild bootstrap for linear models

In Rfast2: A Collection of Efficient and Extremely Fast R Functions II

Cluster robust wild bootstrap for linear models

Description

Cluster robust wild bootstrap for linear models.

Usage

wild.boot(y, x, cluster, ind = NULL, R = 999, parallel = FALSE)

Arguments

y	The dependent variable, a numerical vector with numbers.
x	A matrix or a data.frame with the indendent variables.
cluster	A vector indicating the clusters.
ind	A vector with the indices of the variables for which wild bootstrap p-values will be computed. If NULL (default value), the p-values are computed for each variable.
R	The number of bootstrap replicates to perform.
parallel	Do you want the process to take place in parallel? If yes, then set this equal to TRUE.

Details

A linear regression model for clustered data is fitted. For more information see Chapter 4.21 of Hansen (2019).

Value

A matrix with 5 columns, the estimated coefficients of the linear model, their cluster robust standard error, their cluster robust test statistic, their cluster robust p-value, and their cluster robust wild bootstrap p-value.

Author(s)

Michail Tsagris and Stefanos Fafalios.

R implementation and documentation: Michail Tsagris [e-mail] and Stefanos Fafalios [e-mail].

References

Cameron A. Colin, Gelbach J.B., and Miller D.L. (2008). Bootstrap-Based Improvements for Inference with Clustered Errors. The Review of Economics and Statistics 90(3): 414-427.

See Also

gee.reg, cluster.lm

Examples

y <- rnorm(200)
id <- sample(1:20, 200, replace = TRUE)
x <- matrix( rnorm(200 * 3), ncol = 3 )
wild.boot(y, x, cluster = id)

Rfast2 documentation built on May 29, 2024, 8:45 a.m.