o_delta_boot: Bootstrapped delta*s

In robomit: Robustness Checks for Omitted Variable Bias

Description

Estimates bootstrapped delta*s, i.e., the degree of selection on unobservables relative to observables (with respect to the treatment variable) that would be necessary to eliminate the result (following Oster 2019).

Usage

o_delta_boot(y, x, con, m = "none", w = NULL, id = "none", time = "none", beta = 0, R2max,
sim, obs, rep, 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.
beta	beta for which delta*s should be estimated (default is beta = 0).
R2max	Maximum R-square for which delta*s should be estimated.
sim	Number of simulations.
obs	Number of draws per simulation.
rep	Bootstrapping either with (= TRUE) or without (= FALSE) replacement.
type	Model type (either lm or plm; as string).
useed	User defined seed.
data	Dataset.

Details

Estimates bootstrapped delta*s, i.e., the degree of selection on unobservables relative to observables (with respect to the treatment variable) that would be necessary to eliminate the result (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 bootstrapped delta*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)

# estimate bootstrapped delta*s
o_delta_boot(y = "mpg",          # dependent variable
             x = "wt",           # independent treatment variable
             con = "hp + qsec",  # related control variables
             beta = 0,           # beta
             R2max = 0.9,        # maximum R-square
             sim = 100,          # number of simulations
             obs = 30,           # draws per simulation
             rep = FALSE,        # bootstrapping with or without replacement
             type = "lm",        # model type
             useed = 123,        # seed
             data = data_oster)  # dataset

robomit documentation built on June 22, 2021, 9:09 a.m.