o_beta_boot_inf: Bootstrapped mean beta* and confidence intervals

In robomit: Robustness Checks for Omitted Variable Bias

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

Example output

                   mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
# A tibble: 13 x 2
   Name                 Value
   <chr>                <dbl>
 1 Beta* (mean)        -1.90 
 2 CI_90_low           -3.34 
 3 CI_90_high          -0.456
 4 CI_95_low           -3.62 
 5 CI_95_high          -0.180
 6 CI_99_low           -4.16 
 7 CI_99_high           0.364
 8 Simulations        100    
 9 Observations        30    
10 Max R-square         0.9  
11 Delta (defined)      1    
12 Model: lm           NA    
13 Replacement: FALSE  NA    

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