o_delta {robomit}

delta*

Description

Estimates delta*, 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(y, x, con, m = "none", w = NULL, id = "none", time = "none", beta = 0, R2max,
type, 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* should be estimated (default is beta = 0).

R2max

Maximum R-square for which delta* should be estimated.

type

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

data

Dataset.

Details

Estimates delta*, 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). 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 delta* and various other information.

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 delta*
o_delta(y = "mpg",           # dependent variable
        x = "wt",            # independent treatment variable
        con = "hp + qsec",   # related control variables
        beta = 0,            # beta
        R2max = 0.9,         # maximum R-square
        type = "lm",         # model type
        data = data_oster)   # dataset