robomit (version 1.0.6)

o_beta_boot_inf: 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)

Value

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

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.

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