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")

or

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),
  x1 = runif(n_firms * T),
  x2 = rnorm(n_firms * T),
  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,
  lag_range  = 17,
  covariates = ~ x1 + x2,
  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)

Handling Irregular Time Data

In some panel datasets, the time variable may not be regularly spaced — for example, it may be a Date column or have gaps between observations. In such cases, run_es() provides a convenient option: time_transform = TRUE.

When time_transform is set to TRUE, the function automatically creates a sequential time index (1, 2, 3, …) within each unit (specified by the unit argument) and uses that index for the event study analysis. This is particularly useful when your original time variable is not integer-valued or has irregular spacing.

df_alt <- df |>
  dplyr::mutate(date = as.Date(paste0(year, "-01-01")))

event_study_alt <- run_es(
  data           = df_alt,
  outcome        = y,
  treatment      = is_treated,
  time           = date,
  timing         = 19,        # Corresponds to the 19th time point per unit
  lead_range     = 3,
  lag_range      = 3,
  fe             = ~ firm_id + year,
  cluster        = ~ state_id,
  baseline       = -1,
  time_transform = TRUE,
  unit           = firm_id
)

head(event_study_alt)

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.
  • Accepts covariates specified as a one-sided formula (e.g., ~ x1 + x2).
  • Fixed effects must also be specified as a one-sided formula (e.g., ~ id + year).
  • Supports clustered standard errors:
    • You can specify clusters using either a character vector (e.g., c("id", "year")) or a one-sided formula (e.g., ~ id + year or ~ id^year).
    • The cluster variables are evaluated after subsetting the estimation sample to ensure compatibility.
  • Returns a tidy dataframe with estimated coefficients, standard errors, and confidence intervals.
  • time_transform: If TRUE, replaces the time variable with a sequential index within each unit (e.g., for irregular time variables like Date).
  • unit: Required when time_transform = TRUE. Specifies the panel unit ID (e.g., firm_id).
• 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!🥂