uggs

A package that builds on the bcaboot package to compute bias corrected and accelerated bootstrap confidence limits . By implementing parallel computing using the furrr package, we are able to make substantial speed improvements.

For further information the bca bootstrapping method, refer to

Computer Age Satistical Inference found here
Or the manual for bcaboot

Installation

To install, use devtools::install_github("yixinsun1216/uggs")

Example

 library(lfe)
 library(uggs)
 
 ## create covariates
 x1 <- rnorm(1000)
 x2 <- rnorm(length(x1))
 
 ## fixed effects
 fe <- factor(sample(20, length(x1), replace=TRUE))
 
 ## effects for fe
 fe_effs <- rnorm(nlevels(fe))
 
 ## creating left hand side y
 u <- rnorm(length(x1))
 y <- 2 * x1 + x2 + fe_effs[fe] + u
 
 # create dataframe to pass into uggs
 df_test <- as.data.frame(cbind(y, x1, x2, fe))
 
 # function that returns parameter of interest, x1
 est_test <- function(df){
 	m <- felm(y ~ x1 + x2 | fe, df)
 	as.numeric(coef(m)["x1"])
 }
 
 x1_boot <- uggs(df_test, 1000, est_test, jcount = 40, jreps = 5)
 
 x1_boot
$`limits`
      bca      std      pct   jacksd     
0.025 1.977667 1.975631 0.029 0.004010308
0.05  1.987274 1.985915 0.054 0.001749787
0.1   1.996758 1.99777  0.102 0.004436592
0.5   2.038755 2.039592 0.486 0.001477231
0.9   2.08201  2.081414 0.902 0.002506374
0.95  2.096626 2.09327  0.954 0.004449781
0.975 2.106012 2.103553 0.979 0.001599195

$`stats`
       theta       sdboot          z0          a    sdjack
est 2.039592 0.0326336866 -0.01754730 0.02673691 0.0315359
jsd 0.000000 0.0007583239  0.03020335 0.00000000 0.0000000

$B.mean
[1] 1000.000000    2.039816

$ustats
    ustat       sdu 
2.0393682 0.1367483