This R package implements robust empirical Bayes confidence intervals from
Armstrong, Kolesár, and Plagborg-Møller
(2022). See the package
manual for documentation of the package functions, and the
package vignette for a description of the package and an example
of its usage (available through vignette("ebci")
once the package is
installed). See ebci_matlab for a
Matlab version of this package, and
ebreg for a Stata version.
This software package is based upon work supported by the National Science Foundation under grant numbers SES-2049765 (Armstrong), SES-22049356 (Kolesár), and SES-1851665 (Plagborg-Møller), and by work supported by the Alfred P. Sloan Research Fellowship (Kolesár).
You can install the released version of ebci
from
CRAN with:
install.packages("ebci")
Alternatively, you can get the current development version from GitHub:
if (!requireNamespace("remotes")) {
install.packages("remotes")
}
remotes::install_github("kolesarm/ebci")
Calculation of the critical values used to construct robust empirical Bayes confidence intervals:
library("ebci")
## Usual critical value
cva(m2=0, kappa=Inf, alpha=0.05)
## Larger critical value that takes bias into account. Only uses second moment
## constraint on normalized bias, m2=4.
cva(m2=4, kappa=Inf, alpha=0.05)
## Add a constraint that kurtosis equals 3. This tightens the critical value
cva(m2=4, kappa=3, alpha=0.05)
Estimates and robust EBCIs for neighborhood effects, as in the empirical
application in Armstrong, Kolesár, and Plagborg-Møller
(2020). Shrink fixed-effect estimates of the
neighborhood effects, for children with parents at the 25th percentile of the
income distribution (theta25
) toward average outcome for permanent residents
(stayers) at the 25th percentile of the income distribution. Use precision
weights proportional to the inverse of the squared standard error of the
fixed-effect estimates.
r <- ebci(theta25 ~ stayer25, data=cz, se=se25, weights=1/se25^2)