README.md

In fixes: Tools for Creating and Visualizing Fixed-Effects Event Study Models

fixes

Overview

Note The fixes package currently supports data with annual time intervals only. For datasets with finer time intervals, such as monthly or quarterly data, I recommend creating a new column with sequential time numbers (e.g., 1, 2, 3, …) representing the time order. This column can then be used for analysis.

The fixes package is designed for conducting analysis and creating plots for event studies, a method used to verify the parallel trends assumption in two-way fixed effects (TWFE) difference-in-differences (DID) analysis.

The package includes two main functions:

1. run_es(): Accepts a data frame, generates lead and lag variables, and performs event study analysis. The function returns the results as a data frame.
2. plot_es(): Creates plots using ggplot2 based on the data frame generated by run_es(). Users can choose between a plot with geom_ribbon() or geom_errorbar() to visualize the results.

Installation

You can install the package like so:

# install.packages("pak")
pak::pak("yo5uke/fixes")

or

# install.packages("devtools")
devtools::install_github("yo5uke/fixes")

The fixes package is not currently available on CRAN. Please install it from the GitHub repository.

How to use

First, load the library.

library(fixes)

Data frame

The data frame to be analyzed must include the following variables:

1. A variable to identify individuals.
2. A dummy variable indicating treated individuals (e.g., is_treated).
3. A variable representing time (e.g., year).
4. An outcome variable.

For example, a data frame like the following:

| firm_id | state_id | year | is_treated | y | |--------:|---------:|-----:|-----------:|-----------:| | 1 | 21 | 1980 | 1 | 0.8342158 | | 1 | 21 | 1981 | 1 | -0.5354355 | | 1 | 21 | 1982 | 1 | 1.1372828 | | 1 | 21 | 1983 | 1 | 0.7339165 | | 1 | 21 | 1984 | 1 | 1.4232840 | | 1 | 21 | 1985 | 1 | 1.2783362 |

run_es()

run_es() has nine arguments.

| Arguments | Description | |----|----| | data | Data frame to be used | | outcome | Outcome variable | | treatment | Dummy variable indicating the individual being treated | | time | Variable that represents time | | timing | Variable indicating treatment timing | | lead_range | Range of time before treatment | | lag_range | Range of time after treatment (excluding the year of treatment) | | fe | Variable representing fixed effects | | cluster | A variable that specifies how to cluster the standard error (if clustering is requested) | | baseline | A number indicating the relative year to be dropped when performing a regression | | interval | Parameter to specify the time step between observations (e.g., 1 for yearly data, 5 for 5-year intervals) |

Then, perform the analysis as follows:

event_study <- run_es(
  data       = df, 
  outcome    = y, 
  treatment  = is_treated, 
  time       = year, 
  timing     = 1998, 
  lead_range = 5, 
  lag_range  = 5, 
  fe         = firm_id + year, 
  cluster    = "state_id", 
  baseline   = -1, 
  interval   = 1
)

Note: The fe argument should be specified using additive notation (e.g., firm_id + year), while the cluster argument should be enclosed in double quotation marks.

By executing run_es(), the event study analysis results will be returned as a tidy data frame[^1].

You can use this data to create your own plots, but fixes also provides convenient plotting functions.

plot_es()

The plot_es() function creates a plot based on ggplot2.

plot_es() has 12 arguments.

| Arguments | Description | |----|----| | data | Data frame created by run_es() | | type | The type of confidence interval visualization: “ribbon” (default) or “errorbar” | | vline_val | The x-intercept for the vertical reference line (default: 0) | | vline_color | Color for the vertical reference line (default: “#000”) | | hline_val | The y-intercept for the horizontal reference line (default: 0) | | hline_color | Color for the horizontal reference line (default: “#000”) | | linewidth | The width of the lines for the plot (default: 1) | | pointsize | The size of the points for the estimates (default: 2) | | alpha | The transparency level for ribbons (default: 0.2) | | barwidth | The width of the error bars (default: 0.2) | | color | The color for the lines and points (default: “#B25D91FF”) | | fill | The fill color for ribbons (default: “#B25D91FF”). |

If you don’t care about the details, you can just pass the data frame created with run_es() and the plot will be complete.

plot_es(event_study)

plot_es(event_study, type = "errorbar")

plot_es(event_study, type = "errorbar", vline_val = -.5)

Since it is created on a ggplot2 basis, it is possible to modify minor details.

plot_es(event_study, type = "errorbar") + 
  ggplot2::scale_x_continuous(breaks = seq(-5, 5, by = 1)) + 
  ggplot2::ggtitle("Result of Event Study")

Debugging

If you find an issue, please report it on the GitHub Issues page.

[^1]: Behind the scenes, fixest::feols() is used for estimation.

fixes documentation built on April 3, 2025, 6:08 p.m.