o_delta_rsq_viz: Visualization of deltas* over a range of max R-squares

In robomit: Robustness Checks for Omitted Variable Bias

Description

Estimates and visualizes deltas*, i.e. the degree of selection on unobservables relative to observables that would be necessary to explain away the result, following Oster (2019) over a range of max R-squares.

Usage

o_delta_rsq_viz(y, x, con, id = "none", time = "none", beta = 0, type, data)

Arguments

y	Name of the dependent variable (as string).
x	Name of the independent variable of interest (treatment variable; as string).
con	Name of the other control variables. Provided as string in the format: "w + z +...".
id	Name of the individual id variable (e.g. firm or farm; as string). Only applicable for fixed effect models.
time	Name of the time variable (e.g. year or month; as string). Only applicable for fixed effect models.
beta	Beta for which delta* should be estimated (default is beta = 0).
type	Model type (either lm or plm; as string).
data	Data.

Details

Estimates and visualizes deltas*, i.e. the degree of selection on unobservables relative to observables that would be necessary to explain away the result, following Oster (2019) over a range of max R-squares. The range of max R-squares starts from the R-square of the controlled model rounded up to the next 1/100 to 1. The function supports linear cross sectional (see lm objects in R) and panel fixed effect (see plm objects in R) models.

Value

Returns ggplot object. Including deltas* over a range of max R-squares.

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 and visualize deltas* over a range of max R-squares
o_delta_rsq_viz(y = "mpg",               # define the dependent variable name
                    x = "wt",            # define the main independent variable name
                    con = "hp + qsec",   # other control variables
                    beta = 0,            # define beta. This is usually set to 0
                    type = "lm",         # define model type
                    data = data_oster)   # define dataset

robomit documentation built on Sept. 15, 2020, 5:07 p.m.