o_beta_boot_inf {robomit}

Bootstrapped mean beta* and confidence intervals

Description

Provides the mean and confidence intervals of estimated bootstrapped beta*s, i.e., the bias-adjusted treatment effects (or correlations) (following Oster 2019).

Usage

o_beta_boot_inf(y, x, con, m = "none", w = NULL, id = "none", time = "none",
delta = 1, R2max, sim, obs, rep, CI, type, useed = NA, data)

Arguments

y

Name of the dependent variable (as string).

x

Name of the independent treatment variable (i.e., variable of interest; as string).

con

Name of related control variables. Provided as string in the format: "w + z +...".

m

Name of unrelated control variables (m; see Oster 2019; as string; default is m = "none").

w

weights (only for weighted estimations). Warning: For weighted panel models R can report different R-square than Stata, leading deviation between R and Stata results.

id

Name of the individual id variable (e.g. firm or farm; as string). Only applicable for fixed effect panel models.

time

Name of the time id variable (e.g. year or month; as string). Only applicable for fixed effect panel models.

delta

delta for which beta*s should be estimated (default is delta = 1).

R2max

Maximum R-square for which beta*s should be estimated.

sim

Number of simulations.

obs

Number of draws per simulation.

rep

Bootstrapping either with (= TRUE) or without (= FALSE) replacement

CI

Confidence intervals, indicated as vector. Can be and/or 90, 95, 99.

type

Model type (either lm or plm; as string).

useed

User defined seed.

data

Dataset.

Details

Provides the mean and confidence intervals of estimated bootstrapped beta*s, i.e., the bias-adjusted treatment effects (or correlations) (following Oster 2019). Bootstrapping can either be done with or without replacement. The function supports linear cross-sectional (see lm objects in R) and fixed effect panel (see plm objects in R) models.

Value

Returns tibble object, which includes the mean and confidence intervals of estimated bootstrapped beta*s.

References

Oster, E. (2019). Unobservable Selection and Coefficient Stability: Theory and Evidence. Journal of Business & Economic Statistics, 37, 187-204.

Examples

# load data, e.g. the in-build mtcars dataset
data("mtcars")
data_oster <- mtcars

# preview of data
head(data_oster)

# load robomit
require(robomit)

# compute the mean and confidence intervals of estimated bootstrapped beta*s
o_beta_boot_inf(y = "mpg",            # dependent variable
                x = "wt",             # independent treatment variable
                con = "hp + qsec",    # related control variables
                delta = 1,            # delta
                R2max = 0.9,          # maximum R-square
                sim = 100,            # number of simulations
                obs = 30,             # draws per simulation
                rep = FALSE,          # bootstrapping with or without replacement
                CI = c(90,95,99),     # confidence intervals
                type = "lm",          # model type
                useed = 123,          # seed
                data = data_oster)    # dataset