EventStudyPlot {eventstudyr}

Creates an Event-Study Plot Following the Suggestions in Freyaldenhoven et al. (2021)

Description

EventStudyPlot takes the output from EventStudy() and combines it with additional optional arguments to facilitate constructing an Event-Study Plot.

Usage

EventStudyPlot(
  estimates,
  xtitle = "Event time",
  ytitle = "Coefficient",
  ybreaks = NULL,
  conf_level = 0.95,
  supt = 0.95,
  num_sim = 1000,
  add_mean = FALSE,
  pre_event_coeffs = TRUE,
  post_event_coeffs = TRUE,
  add_zero_line = TRUE,
  smpath = FALSE
)

Arguments

estimates

The output from calling EventStudy(). Should be a list of length 2.

xtitle

The title for the x-axis. Should be a string. Defaults to "Event time".

ytitle

The title for the y-axis. Should be a string. Defaults to "Coefficient".

ybreaks

A vector containing the desired breaks for the y-axis. Defaults to NULL, which means the breaks are computed automatically. If custom breaks are selected with the add_mean argument set to TRUE, then the breaks must include zero.

conf_level

Confidence level used for confidence interval expressed as a real number between 0 and 1, inclusive. Defaults to 0.95.

supt

The confidence level used for obtaining the sup-t bands critical value. Should be a real number between 0 and 1, inclusive. Defaults to .95. Sup-t bands are simulation-based, so you must set a seed if you would like your sup-t band results to be reproducible (see examples).

num_sim

The number of simulations used in generating the sup-t bands. Should be a natural number. Defaults to 1000.

add_mean

Adds the mean of the dependent variable in the period used for normalization. Should be TRUE or FALSE. Defaults to FALSE.

pre_event_coeffs

If TRUE, uses pre and overidpre from estimates to test for pre-trends. Should be TRUE or FALSE. Defaults to TRUE.

post_event_coeffs

If TRUE, uses post and overidpost from estimates to test for leveling-off. Should be TRUE or FALSE. Defaults to TRUE.

add_zero_line

Whether or not to plot a dashed horizontal line at y = 0. Should be TRUE or FALSE. Defaults to TRUE, meaning the line is plotted.

smpath

Plot smoothest path of confounder that rationalizes event study coefficients. Should be TRUE or FALSE. Defaults to FALSE.

Value

The Event-Study plot as a ggplot2 object.

Examples

#

# Minimal examples
### OLS

estimates_ols <- EventStudy(
   estimator = "OLS",
   data = example_data,
   outcomevar = "y_smooth_m",
   policyvar = "z",
   idvar = "id",
   timevar = "t",
   controls = "x_r",
   FE = TRUE, TFE = TRUE,
   post = 3, overidpost = 5,
   pre = 2,  overidpre = 4,
   normalize = - 3
)

plt_ols <- EventStudyPlot(estimates = estimates_ols)
plt_ols

### IV

estimates_fhs <- EventStudy(
   estimator = "FHS",
   data = example_data,
   outcomevar = "y_smooth_m",
   policyvar = "z",
   idvar = "id",
   timevar = "t",
   proxy = "x_r",
   post = 2, overidpost = 1,
   pre = 0,  overidpre = 3,
   normalize = -1
)

plt_fhs <- EventStudyPlot(estimates = estimates_fhs)
plt_fhs

# Optional arguments

### Change x- and y-axis titles and set ybreaks
EventStudyPlot(estimates = estimates_ols,
               xtitle = "Relative time", ytitle = "",
               ybreaks = seq(-2, 1, 0.5))

### Add smoothest path
EventStudyPlot(estimates = estimates_ols, smpath = TRUE)

### Add y-mean to y-axis and line y = 0
EventStudyPlot(estimates = estimates_ols, add_mean = TRUE,
               add_zero_line = TRUE)

### Do not plot supt bands
EventStudyPlot(estimates = estimates_ols, supt = NULL)

### Setting seed prior to plotting sup-t bands
set.seed(1234)
EventStudyPlot(estimates = estimates_ols)

# Modify plots using ggplot2 functions
library(ggplot2)

### Change color of dots, horizontal line, and theme
plt_ols +
  geom_point(color = "red") +
  geom_hline(color = "gray", yintercept = 0) +
  theme_light() +
  theme(panel.grid.minor.x = element_blank())