ecic {ecic}

Estimate a changes-in-changes model with multiple periods and cohorts

Description

Calculates a changes-in-changes model as in Athey and Imbens (2006) for multiple periods and cohorts.

Usage

ecic(
  yvar = NULL,
  gvar = NULL,
  tvar = NULL,
  ivar = NULL,
  dat = NULL,
  myProbs = seq(0.1, 0.9, 0.1),
  nMin = 40,
  boot = c("weighted", "normal", "no"),
  nReps = 10,
  weight_n0 = c("n1", "n0"),
  weight_n1 = c("n1", "n0"),
  quant_algo = 1,
  es = FALSE,
  n_digits = NULL,
  periods_es = NULL,
  save_to_temp = FALSE,
  progress_bar = c("progress", "void", "cli"),
  nCores = 1
)

Arguments

yvar

Dependent variable.

gvar

Group variable. Can be either a string (e.g., "first_treated") or an expression (e.g., first_treated). In a staggered treatment setting, the group variable typically denotes treatment cohort.

tvar

Time variable. Can be a string (e.g., "year") or an expression (e.g., year).

ivar

Individual Index variable. Can be a string (e.g., "country") or an expression (e.g., country). Only needed to check cohort sizes.

dat

The data set.

myProbs

Quantiles that the quantile treatment effects should be calculated for.

nMin

Minimum observations per groups. Small groups are deleted.

boot

Bootstrap. Resampling is done over the entire data set ("normal"), but might be weighted by period-cohort size ("weighted"). If you do not want to calculate standard error, set boot = "no".

nReps

Number of bootstrap replications.

weight_n0

Weight for the aggregation of the CDFs in the control group. n1 uses cohort sizes (Alternative: n0).

weight_n1

Weight for the aggregation of the CDFs in the treatment group. n1 uses cohort sizes (Alternative: n0).

quant_algo

Quantile algorithm (see Wikipedia for definitions).

es

Event Study (Logical). If TRUE, a quantile treatment effect is estimated for each event-period.

n_digits

Rounding the dependent variable before aggregating the empirical CDFs reduces the size of the imputation grid. This can significantly reduce the amount of RAM used in large data sets and improve running time, while reducing precision (Use with caution).

periods_es

Periods of the event study.

save_to_temp

Logical. If TRUE, results are temporarily saved. This reduces the RAM needed, but increases running time.

progress_bar

Whether progress bar should be printed (select "void" for no progress bar or "cli" for another type of bar).

nCores

Number of cores used. If set > 1, bootstrapping will run in parallel.

Value

An ecic object.

References

Athey, Susan and Guido W. Imbens (2006). Identification and Inference in Nonlinear Difference-in-Differences Models. doi:10.1111/j.1468-0262.2006.00668.x

Examples

# Example 1. Using the small mpdta data in the did package
data(dat, package = "ecic")
dat = dat[dat$first.treat <= 1983 & dat$countyreal <= 1000,] # small data for fast run

mod_res = 
  summary(
  ecic(
    yvar  = lemp,         # dependent variable
    gvar  = first.treat,  # group indicator
    tvar  = year,         # time indicator
    ivar  = countyreal,   # unit ID
    dat   = dat,          # dataset
    boot  = "normal",     # bootstrap proceduce ("no", "normal", or "weighted")
    nReps = 3             # number of bootstrap runs
    )
    )

# Basic Plot
ecic_plot(mod_res)


# Example 2. Load some larger sample data
data(dat, package = "ecic")

# Estimate a basic model with the package's sample data
mod_res =
  summary(
  ecic(
    yvar  = lemp,         # dependent variable
    gvar  = first.treat,  # group indicator
    tvar  = year,         # time indicator
    ivar  = countyreal,   # unit ID
    dat   = dat,          # dataset
    boot  = "weighted",   # bootstrap proceduce ("no", "normal", or "weighted")
    nReps = 20            # number of bootstrap runs
  )
  )
  
# Basic Plot
ecic_plot(mod_res)

# Example 3. An Event-Study Example
mod_res =
  summary(
  ecic(
    es    = TRUE,         # aggregate for every event period
    yvar  = lemp,         # dependent variable
    gvar  = first.treat,  # group indicator
    tvar  = year,         # time indicator
    ivar  = countyreal,   # unit ID
    dat   = dat,          # dataset
    boot  = "weighted",   # bootstrap proceduce ("no", "normal", or "weighted")
    nReps = 20            # number of bootstrap runs
  )
  )
  
# Plots
ecic_plot(mod_res) # aggregated in one plot
ecic_plot(mod_res, es_type = "for_quantiles") # individually for every quantile
ecic_plot(mod_res, es_type = "for_periods")   # individually for every period