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In DiDforBigData: A Big Data Implementation of Difference-in-Differences Estimation with Staggered Treatment

knitr::opts_chunk$set(
  collapse = TRUE,
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There are only 3 functions in this package:

1. SimDiD(): This function simulates data.
2. DiDge(): This function estimates DiD for a single cohort and a single event time.
3. DiD(): This function estimates DiD for all available cohorts and event times.

We now demonstrate the simplest application of the 3 functions.

Detailed documentation for each of these function is available from the Reference tab above.

0. Installation

devtools::install_github("setzler/DiDforBigData")

library(DiDforBigData)

1. Prepare Data

I provide a simple data simulator as follows:

sim = SimDiD(sample_size = 400, seed=123)

# true ATTs in the simulation
print(sim$true_ATT)

# simulated data
simdata = sim$simdata
print(simdata)

Your real data needs to have this "long" format, i.e., there need to be variables for the individual identifier (e.g. id), the time variable (e.g. year), the cohort at which treatment begins (e.g. cohort), and the outcome variable (e.g. Y). No other variables are required. These variables can have any names you prefer.

Before going to the estimation, we need to prepare a list of the variable names:

varnames = list()
varnames$time_name = "year" 
varnames$outcome_name = "Y"
varnames$cohort_name = "cohort"
varnames$id_name = "id"

2. Estimate DiD for a Single Cohort

We choose an event time (+3) and a cohort of treated units (2010), then estimate DiD:

did_2010 = DiDge(inputdata = simdata, varnames = varnames, 
             cohort_time = 2010, event_postperiod = 3)

print(did_2010)

Comparing this estimate to the true ATT above, we see that the estimation performed well.

Note that it used -1 as the base year by default. This is easy to change.

3. Estimate DiD for All Cohorts and Event Times

Suppose we want to estimate the ATT at each event time from -3 to +5. We can do so as follows:

did_all = DiD(inputdata = simdata, varnames = varnames, min_event = -3, max_event = 5)

The output of DiD() is a list. One object in the list is results_average, which includes the average ATT across cohorts:

print(did_all$results_average)

The other output from DiD() is results_cohort, which includes all combinations of event times and cohorts. It is too large to print here, so let's just print the results for event times 1 and 2:

print(did_all$results_cohort[EventTime==1 | EventTime==2])

Note: the simulated data ends in 2013, so event time 2 is not available for treatment cohort 2012.

To take an average across multiple event times, use the Esets argument. It accepts a list, in which each item is a vector of event times over which to average:

did_all = DiD(inputdata = simdata, varnames = varnames, min_event = -3, max_event = 5, 
              Esets = list(c(1,2), c(1,2,3)))

print(did_all$results_Esets)

DiDforBigData documentation built on April 3, 2023, 5:22 p.m.