did_imputation: Borusyak, Jaravel, and Spiess (2021) Estimator

In didimputation: Imputation Estimator from Borusyak, Jaravel, and Spiess (2021)

Borusyak, Jaravel, and Spiess (2021) Estimator

Description

Treatment effect estimation and pre-trend testing in staggered adoption diff-in-diff designs with an imputation approach of Borusyak, Jaravel, and Spiess (2021)

Usage

did_imputation(
  data,
  yname,
  gname,
  tname,
  idname,
  first_stage = NULL,
  wname = NULL,
  wtr = NULL,
  horizon = NULL,
  pretrends = NULL,
  cluster_var = NULL
)

Arguments

data	A data.frame
yname	String. Variable name for outcome. Use fixest c() syntax for multiple lhs, e.g. "c(y1, y2)".
gname	String. Variable name for unit-specific date of treatment (never-treated should be zero or NA).
tname	String. Variable name for calendar period.
idname	String. Variable name for unique unit id.
first_stage	Formula for Y(0). Formula following fixest::feols. Fixed effects specified after "|". If not specified, then just unit and time fixed effects will be used.
wname	String. Variable name for estimation weights of observations. This is used in estimating Y(0) and also augments treatment effect weights.
wtr	Character vector of treatment weight names (see horizon for standard static and event-study weights)
horizon	Integer vector of event_time or TRUE. This only applies if wtr is left as NULL. if specified, weighted averages/sums of treatment effects will be reported for each of these horizons separately (i.e. tau0 for the treatment period, tau1 for one period after treatment, etc.). If TRUE, all horizons are used. If wtr and horizon are null, then the static treatment effect is calculated.
pretrends	Integer vector or TRUE. Which pretrends to estimate. If TRUE, all pretrends are used.
cluster_var	String. Variable name for clustering groups. If not supplied, then idname is used as default.

Details

The imputation-based estimator is a method of calculating treatment effects in a difference-in-differences framework. The method estimates a model for Y(0) using untreated/not-yet-treated observations and predicts Y(0) for the treated observations hat(Y_it(0)). The difference between treated and predicted untreated outcomes Y_it(1) - hat(Y_it(0)) serves as an estimate for the treatment effect for unit i in period t. These are then averaged to form average treatment effects for groups of (i, t).

Value

A data.frame containing treatment effect term, estimate, standard error and confidence interval. This is in tidy format.

Examples

Load example dataset which has two treatment groups and homogeneous treatment effects

# Load Example Dataset
data("df_hom", package="didimputation")

Static TWFE

You can run a static TWFE fixed effect model for a simple treatment indicator

did_imputation(data = df_hom, yname = "dep_var", gname = "g",
               tname = "year", idname = "unit")
#>      term estimate  std.error conf.low conf.high
#>    <char>    <num>      <num>    <num>     <num>
#> 1:  treat 2.024639 0.03243596 1.961065  2.088214

Event Study

Or you can use relative-treatment indicators to estimate an event study estimate

did_imputation(data = df_hom, yname = "dep_var", gname = "g",
               tname = "year", idname = "unit", horizon=TRUE)
#>       term estimate  std.error conf.low conf.high
#>     <char>    <num>      <num>    <num>     <num>
#>  1:      0 2.117232 0.07368419 1.972811  2.261653
#>  2:      1 1.856536 0.07672104 1.706163  2.006909
#>  3:      2 1.986357 0.07137180 1.846468  2.126246
#>  4:      3 2.004843 0.07653409 1.854836  2.154850
#>  5:      4 1.950228 0.07543636 1.802372  2.098083
#>  6:      5 2.038302 0.07580288 1.889728  2.186875
#>  7:      6 2.031571 0.07223098 1.889999  2.173144
#>  8:      7 2.025286 0.07541719 1.877468  2.173104
#>  9:      8 1.976081 0.07493409 1.829210  2.122951
#> 10:      9 2.121434 0.07268404 1.978974  2.263895
#> 11:     10 2.087984 0.08271442 1.925864  2.250105
#> 12:     11 1.942825 0.11421421 1.718965  2.166685
#> 13:     12 1.940532 0.11200348 1.721005  2.160059
#> 14:     13 1.964569 0.11361969 1.741875  2.187264
#> 15:     14 2.023456 0.11753255 1.793092  2.253820
#> 16:     15 2.235051 0.12110086 1.997693  2.472409
#> 17:     16 2.178438 0.11552325 1.952013  2.404864
#> 18:     17 1.935576 0.11278311 1.714521  2.156631
#> 19:     18 2.134953 0.10993120 1.919488  2.350418
#> 20:     19 2.111984 0.11146282 1.893517  2.330451
#> 21:     20 1.925168 0.11214206 1.705370  2.144967
#>       term estimate  std.error conf.low conf.high

Example from Cheng and Hoekstra (2013)

Here's an example using data from Cheng and Hoekstra (2013)

# Castle Data
castle = haven::read_dta("https://github.com/scunning1975/mixtape/raw/master/castle.dta")

did_imputation(data = castle, yname = "c(l_homicide, l_assault)", gname = "effyear",
              first_stage = ~ 0 | sid + year,
              tname = "year", idname = "sid")
#> Key: <lhs>
#>           lhs   term   estimate  std.error    conf.low conf.high
#>        <char> <char>      <num>      <num>       <num>     <num>
#> 1:  l_assault  treat 0.04955260 0.05132258 -0.05103966 0.1501449
#> 2: l_homicide  treat 0.07980155 0.06088398 -0.03953105 0.1991341

didimputation documentation built on Dec. 17, 2025, 9:06 a.m.