Description Usage Arguments Details Value References Examples

View source: R/robomit_functions.R

Estimates and visualizes bootstrapped betas*, i.e. the estimated bias-adjusted treatment effects, following Oster (2019).

1 2 3 |

`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. |

`delta` |
Delta for which beta* should be estimated (default is delta = 1). |

`R2max` |
Max R-square for which beta* 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 |

`norm` |
Option to include a normal distribution in the plot (default is norm = TURE). |

`bin` |
Number of bins used for 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` |
Seed number defined by user. |

`data` |
Data. |

Estimates and visualizes bootstrapped betas*, i.e. the estimated bias-adjusted treatment effects, following Oster (2019). Bootstrapping can either be done with or without replacement. The function supports linear cross sectional (see *lm* objects in R) and panel fixed effect (see *plm* objects in R) models.

Returns ggplot object. Including bootstrapped betas*.

Oster, E. (2019). Unobservable selection and coefficient stability: Theory and evidence. Journal of Business & Economic Statistics, 37, 187-204.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
# 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 betas*
o_beta_boot_viz(y = "mpg", # define the dependent variable name
x = "wt", # define the main independent variable name
con = "hp + qsec", # other control variables
delta = 1, # define delta This is usually set to 1
R2max = 0.9, # define the max R-square.
sim = 100, # define number of simulations
obs = 30, # define number of drawn observations per simulation
rep = FALSE, # define if bootstrapping is with or without replacement
CI = c(90,95,99), # define confidence intervals.
type = "lm", # define model type
norm = TRUE, # include normal distribution
bin = 200, # set number of bins
useed = 123, # define seed
data = data_oster) # define dataset
``` |

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