o_beta_boot_inf: Bootstrapped mean beta* and confidence intervals
In robomit: Robustness Checks for Omitted Variable Bias

Description Usage Arguments Details Value References Examples

Provides the mean and confidence intervals of estimated bootstrapped beta*s, i.e., the bias-adjusted treatment effects (or correlations) (following Oster 2019).

1 2	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)

`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.

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.

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

Oster, E. (2019). Unobservable Selection and Coefficient Stability: Theory and Evidence. Journal of Business & Economic Statistics, 37, 187-204.

# 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

                   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