,
ETH Zurich [cph]| Title: | Robustness Checks for Omitted Variable Bias |
|---|---|
| Description: | Robustness checks for omitted variable bias. The package includes robustness checks proposed by Oster (2019). robomit the estimate i) the bias-adjusted treatment correlation or effect and ii) the degree of selection on unobservables relative to observables (with respect to the treatment variable) that would be necessary to eliminate the result based on the framework by Oster (2019). Additionally, robomit offers a set of sensitivity analysis and visualization functions. See: Oster, E. 2019. <doi:10.1080/07350015.2016.1227711>. |
| Authors: | Sergei Schaub [aut, cre] ,
ETH Zurich [cph] |
| Maintainer: | Sergei Schaub <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.0.6 |
| Built: | 2025-02-27 03:46:33 UTC |
| Source: | https://github.com/seschaub/robomit |
Estimates beta*, i.e., the bias-adjusted treatment effect (or correlation) (following Oster 2019).
o_beta(y, x, con, m = "none", w = NULL, id = "none", time = "none", delta = 1, R2max, type, data)o_beta(y, x, con, m = "none", w = NULL, id = "none", time = "none", delta = 1, R2max, type, 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* should be estimated (default is delta = 1). |
R2max |
Maximum R-square for which beta* should be estimated. |
type |
Model type (either lm or plm; as string). |
data |
Dataset. |
Estimates beta*, i.e., the bias-adjusted treatment effect (or correlation).
Returns tibble object, which includes beta* and various other information.
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) # estimate beta* o_beta(y = "mpg", # dependent variable x = "wt", # independent treatment variable con = "hp + qsec", # related control variables delta = 1, # delta R2max = 0.9, # maximum R-square type = "lm", # model type data = data_oster) # dataset# 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 beta* o_beta(y = "mpg", # dependent variable x = "wt", # independent treatment variable con = "hp + qsec", # related control variables delta = 1, # delta R2max = 0.9, # maximum R-square type = "lm", # model type data = data_oster) # dataset
Estimates bootstrapped beta*s, i.e., the bias-adjusted treatment effects (or correlations) (following Oster 2019).
o_beta_boot(y, x, con, m = "none", w = NULL, id = "none", time = "none", delta = 1, R2max, sim, obs, rep, type, useed = NA, data)o_beta_boot(y, x, con, m = "none", w = NULL, id = "none", time = "none", delta = 1, R2max, sim, obs, rep, 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. |
type |
Model type (either lm or plm; as string). |
useed |
User defined seed. |
data |
Dataset. |
Estimates 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 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) # estimate bootstrapped beta*s o_beta_boot(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 type = "lm", # model type useed = 123, # seed data = data_oster) # dataset# 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 beta*s o_beta_boot(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 type = "lm", # model type useed = 123, # seed data = data_oster) # dataset
Provides the mean and confidence intervals of estimated bootstrapped beta*s, i.e., the bias-adjusted treatment effects (or correlations) (following Oster 2019).
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)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# 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
Estimates and visualizes bootstrapped beta*s, i.e., the bias-adjusted treatment effects (or correlations) (following Oster 2019).
o_beta_boot_viz(y, x, con, m = "none", w = NULL, id = "none", time = "none", delta = 1, R2max, sim, obs, rep, CI, type, norm = TRUE, bin, col = c("#08306b","#4292c6","#c6dbef"), nL = TRUE, mL = TRUE, useed = NA, data)o_beta_boot_viz(y, x, con, m = "none", w = NULL, id = "none", time = "none", delta = 1, R2max, sim, obs, rep, CI, type, norm = TRUE, bin, col = c("#08306b","#4292c6","#c6dbef"), nL = TRUE, mL = TRUE, 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). |
norm |
Option to include a normal distribution in the plot (default is norm = TURE). |
bin |
Number of bins used in the histogram. |
col |
Colors used to indicate different confidence interval levels (indicated as vector). Needs to be the same length as the variable CI. The default is a blue color range. |
nL |
Option to include a red vertical line at 0 (default is nL = TRUE). |
mL |
Option to include a vertical line at mean of all beta*s (default is mL = TRUE). |
useed |
User defined seed. |
data |
Dataset. |
Estimates and visualizes 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 ggplot2 object, which depicts the 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) # estimate and visualize bootstrapped beta*s o_beta_boot_viz(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 norm = TRUE, # normal distribution bin = 200, # number of bins useed = 123, # seed data = data_oster) # dataset# 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 bootstrapped beta*s o_beta_boot_viz(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 norm = TRUE, # normal distribution bin = 200, # number of bins useed = 123, # seed data = data_oster) # dataset
Estimates beta*s, i.e., the bias-adjusted treatment effects (or correlations) (following Oster 2019) over a range of maximum R-squares.
o_beta_rsq(y, x, con, m = "none", w = NULL, id = "none", time = "none", delta = 1, type, data)o_beta_rsq(y, x, con, m = "none", w = NULL, id = "none", time = "none", delta = 1, type, 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). |
type |
Model type (either lm or plm; as string). |
data |
Dataset. |
Estimates beta*s, i.e., the bias-adjusted treatment effects (or correlations) (following Oster 2019) over a range of maximum R-squares. The range of maximum 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 fixed effect panel (see plm objects in R) models.
Returns tibble object, which includes beta*s over a range of maximum R-squares.
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) # estimate delta*s over a range of maximum R-squares o_beta_rsq(y = "mpg", # dependent variable x = "wt", # independent treatment variable con = "hp + qsec", # related control variables delta = 1, # delta type = "lm", # model type data = data_oster) # dataset# 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*s over a range of maximum R-squares o_beta_rsq(y = "mpg", # dependent variable x = "wt", # independent treatment variable con = "hp + qsec", # related control variables delta = 1, # delta type = "lm", # model type data = data_oster) # dataset
Estimates and visualizes beta*s, i.e., the bias-adjusted treatment effects (or correlations) (following Oster 2019) over a range of maximum R-squares.
o_beta_rsq_viz(y, x, con, m = "none", w = NULL, id = "none", time = "none", delta = 1, type, data)o_beta_rsq_viz(y, x, con, m = "none", w = NULL, id = "none", time = "none", delta = 1, type, 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). |
type |
Model type (either lm or plm; as string). |
data |
Dataset. |
Estimates and visualizes beta*s, i.e., the bias-adjusted treatment effects (or correlations) (following Oster 2019) over a range of maximum R-squares. The range of maximum 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 fixed effect panel (see plm objects in R) models.
Returns ggplot2 object, which depicts beta*s over a range of maximum R-squares.
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) # estimate and visualize beta*s over a range of maximum R-squares o_beta_rsq_viz(y = "mpg", # dependent variable x = "wt", # independent treatment variable con = "hp + qsec", # related control variables delta = 1, # delta type = "lm", # model type data = data_oster) # dataset# 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 beta*s over a range of maximum R-squares o_beta_rsq_viz(y = "mpg", # dependent variable x = "wt", # independent treatment variable con = "hp + qsec", # related control variables delta = 1, # delta type = "lm", # model type data = data_oster) # dataset
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).
o_delta(y, x, con, m = "none", w = NULL, id = "none", time = "none", beta = 0, R2max, type, data)o_delta(y, x, con, m = "none", w = NULL, id = "none", time = "none", beta = 0, R2max, type, 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. |
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. |
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.
Returns tibble object, which includes delta* and various other information.
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) # 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# 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
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).
o_delta_boot(y, x, con, m = "none", w = NULL, id = "none", time = "none", beta = 0, R2max, sim, obs, rep, type, useed = NA, data)o_delta_boot(y, x, con, m = "none", w = NULL, id = "none", time = "none", beta = 0, R2max, sim, obs, rep, 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. |
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. |
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.
Returns tibble object, which includes bootstrapped delta*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) # 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# 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
Provides the mean and confidence intervals of 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).
o_delta_boot_inf(y, x, con, m = "none", w = NULL, id = "none", time = "none", beta = 0, R2max, sim, obs, rep, CI, type, useed = NA, data)o_delta_boot_inf(y, x, con, m = "none", w = NULL, id = "none", time = "none", beta = 0, 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. |
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 |
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 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.
Returns tibble object, which includes the mean and confidence intervals of bootstrapped delta*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 delta*s o_delta_boot_inf(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 CI = c(90,95,99), # confidence intervals type = "lm", # model type useed = 123, # seed data = data_oster) # dataset# 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 delta*s o_delta_boot_inf(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 CI = c(90,95,99), # confidence intervals type = "lm", # model type useed = 123, # seed data = data_oster) # dataset
Estimates and visualizes 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).
o_delta_boot_viz(y, x, con, m = "none", w = NULL, id = "none", time = "none", beta = 0, R2max, sim, obs, rep, CI, type, norm = TRUE, bin, col = c("#08306b","#4292c6","#c6dbef"), nL = TRUE, mL = TRUE, useed = NA, data)o_delta_boot_viz(y, x, con, m = "none", w = NULL, id = "none", time = "none", beta = 0, R2max, sim, obs, rep, CI, type, norm = TRUE, bin, col = c("#08306b","#4292c6","#c6dbef"), nL = TRUE, mL = TRUE, 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. |
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 |
CI |
Confidence intervals, indicated as vector. Can be and/or 90, 95, 99. |
type |
Model type (either lm or plm; as string). |
norm |
Option to include a normal distribution in the plot (default is norm = TURE). |
bin |
Number of bins used in the histogram. |
col |
Colors used to indicate different confidence interval levels (indicated as vector). Needs to be the same length as the variable CI. The default is a blue color range. |
nL |
Option to include a red vertical line at 0 (default is nL = TRUE). |
mL |
Option to include a vertical line at beta* mean (default is mL = TRUE). |
useed |
User defined seed. |
data |
Dataset. |
Estimates and visualizes 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.
Returns ggplot2 object, which depicts the bootstrapped delta*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) # estimate and visualize bootstrapped delta*s o_delta_boot_viz(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 CI = c(90,95,99), # confidence intervals type = "lm", # model type norm = TRUE, # normal distribution bin = 200, # number of bins useed = 123, # seed data = data_oster) # dataset# 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 bootstrapped delta*s o_delta_boot_viz(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 CI = c(90,95,99), # confidence intervals type = "lm", # model type norm = TRUE, # normal distribution bin = 200, # number of bins useed = 123, # seed data = data_oster) # dataset
Estimates 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) over a range of maximum R-squares following Oster (2019).
o_delta_rsq(y, x, con, m = "none", w = NULL, id = "none", time = "none", beta = 0, type, data)o_delta_rsq(y, x, con, m = "none", w = NULL, id = "none", time = "none", beta = 0, type, 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. |
beta |
beta for which delta*s should be estimated (default is beta = 0). |
type |
Model type (either lm or plm; as string). |
data |
Dataset. |
Estimates 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) over a range of maximum R-squares. The range of maximum 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 fixed effect panel (see plm objects in R) models.
Returns tibble object, which includes delta*s over a range of maximum R-squares.
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) # estimate delta*s over a range of maximum R-squares o_delta_rsq(y = "mpg", # dependent variable x = "wt", # independent treatment variable con = "hp + qsec", # related control variables beta = 0, # beta type = "lm", # model type data = data_oster) # dataset# 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*s over a range of maximum R-squares o_delta_rsq(y = "mpg", # dependent variable x = "wt", # independent treatment variable con = "hp + qsec", # related control variables beta = 0, # beta type = "lm", # model type data = data_oster) # dataset
Estimates and visualizes 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) over a range of maximum R-squares.
o_delta_rsq_viz(y, x, con, m = "none", w = NULL, id = "none", time = "none", beta = 0, type, data)o_delta_rsq_viz(y, x, con, m = "none", w = NULL, id = "none", time = "none", beta = 0, type, 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. |
beta |
beta for which delta*s should be estimated (default is beta = 0). |
type |
Model type (either lm or plm; as string). |
data |
Dataset. |
Estimates and visualizes 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) over a range of maximum R-squares. The range of maximum 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 fixed effect panel (see plm objects in R) models.
Returns ggplot2 object, which depicts delta*s over a range of maximum R-squares.
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) # estimate and visualize delta*s over a range of maximum R-squares o_delta_rsq_viz(y = "mpg", # dependent variable x = "wt", # independent treatment variable con = "hp + qsec", # related control variables beta = 0, # beta type = "lm", # model type data = data_oster) # dataset# 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 delta*s over a range of maximum R-squares o_delta_rsq_viz(y = "mpg", # dependent variable x = "wt", # independent treatment variable con = "hp + qsec", # related control variables beta = 0, # beta type = "lm", # model type data = data_oster) # dataset