Introduction to fixes

Introduction

The fixes package provides a set of tools for creating, estimating, and visualizing event study models using fixed effects regression. With fixes, you can easily generate lead and lag dummy variables, estimate a fixed effects model via fixest::feols(), and visualize the results using ggplot2.

This vignette walks you through a minimal example to demonstrate the core functionality of the package.

Installation

You can install the released version from CRAN:

pak::pak("fixes")

Alternatively, you can use:

install.packages("fixes")

To install the development version directly from GitHub, run:

pak::pak("yo5uke/fixes")

devtools::install_github("yo5uke/fixes")

A Minimal Example

Below is a simple example to simulate a small panel dataset and perform an event study analysis. In this example, we assume an event (treatment) occurs in 2005.

library(fixes)
library(tibble)
library(dplyr)

# Simulate panel data
set.seed(2)

n_firms <- 1000
n_states <- 50
T <- 36

firm_id <- 1:n_firms
state_id <- sample(n_states, size = n_firms, replace = TRUE)
year <- 1980:2015

fe_firm <- rnorm(n_firms, mean = 0, sd = .5)
fe_year <- rnorm(T, mean = 0, sd = .5)
error <- rnorm(n_firms * T, mean = 0, sd = .5)

treated_1998 <- sample(c(1, 0), size = n_firms, 
                       replace = TRUE, prob = c(1/2, 1/2))

df <- tibble::tibble(
  firm_id = rep(firm_id, each = T), 
  state_id = rep(state_id, each = T), 
  year = rep(year, times = n_firms), 
  fe_firm = rep(fe_firm, each = T), 
  fe_year = rep(fe_year, times = n_firms), 
  error = error, 
  is_treated = rep(treated_1998, each = T), 
  after_treat = dplyr::if_else(is_treated == 1 & year >= 1998, 1, 0), 
  y = dplyr::case_when(
    after_treat == 1 ~ 
      rnorm(n_firms * T, mean = .3, sd = .2) * (year - 1997) + fe_firm + fe_year + error, 
    .default = fe_firm + fe_year + error
  )
)


# Run the event study
event_study <- run_es(
  data       = df, 
  outcome    = y, 
  treatment  = is_treated, 
  time       = year, 
  timing     = 1998, 
  lead_range = 18,              # Corresponds to years 2000-2004 (relative time: -5 to -1)
  lag_range  = 17,              # Corresponds to years 2006-2009 (relative time: 1 to 4)
  fe         = firm_id + year, 
  cluster    = "state_id", 
  baseline   = -1, 
  interval   = 1
)

# View the first few rows of the event study results
head(event_study)

Visualizing the Event Study Results

The fixes package provides the plot_es() function to create visualizations of your event study results. Here are some examples:

# Basic plot using ribbon-style confidence intervals
p1 <- plot_es(event_study)
print(p1)

# Plot with error bars instead of a ribbon
p2 <- plot_es(event_study, type = "errorbar")
print(p2)

# Plot with a custom vertical reference line
p3 <- plot_es(event_study, type = "errorbar", vline_val = -0.5)
print(p3)

# Customized plot: adjust axis breaks and add a title
library(ggplot2)
p4 <- plot_es(event_study, type = "errorbar") + 
  scale_x_continuous(breaks = seq(-5, 4, by = 1)) + 
  ggtitle("Result of Event Study")
print(p4)

Package Details

The fixes package is designed for panel data analysis and supports custom time intervals. Key functions include:

run_es():
- Automatically generates lead and lag dummy variables relative to the treatment event.
- Constructs and estimates a fixed effects regression model using fixest::feols().
- Returns a tidy dataframe with estimated coefficients, standard errors, and confidence intervals.
plot_es():
- Provides a flexible visualization tool using ggplot2.
- Offers both ribbon-style and error bar representations of confidence intervals.
- Can be further customized with standard ggplot2 functions.

Conclusion

The fixes package streamlines the process of performing event study analyses with fixed effects. It simplifies the estimation and visualization steps, making it easier to interpret dynamic treatment effects in panel data.

For more information, consult the package documentation:

?run_es
?plot_es

Happy analyzing!🥂