StagDiDModels.jl

(Some) modern staggered difference-in-differences estimators for Julia.

Overview

StagDiDModels.jl implements some of the recent advances in difference-in-differences estimation that address heterogeneous treatment effects in staggered treatment adoption. All estimators provide standard errors, model diagnostics, and are compatible with the StatsAPI ecosystem. Integrates nicely with HonestDiD.jl

Available Estimators

Estimator	Function	Reference
BJS Imputation	`fit_bjs()`	Borusyak, Jaravel, Spiess (2023)
Gardner Two-Stage	`fit_gardner_static()`, `fit_gardner_dynamic()`	Gardner (2021)
Sun-Abraham	`fit_sunab()`	Sun & Abraham (2021)
TWFE	`fit_twfe_static()`, `fit_twfe_dynamic()`	Standard approach

Installation

using Pkg
Pkg.add(url="https://github.com/eohne/StagDiDModels.jl")

using StagDiDModels
using DataFrames

Data Requirements

Panel data with the following variables:

Unit identifier (e.g., firm, individual)
Time period (integer, e.g., year, quarter)
Treatment timing (when unit first receives treatment)
Outcome variable

Never-treated units must be coded as g = 0 or g = missing.

Basic Usage

BJS Imputation Estimator

Static average treatment effect:

julia> fit_bjs_static(df; y=:dep_var, id=:unit, t=:year, g=:g,cluster=:unit)
                 BJSModel
==========================================
Number of obs: 31000  Outcome:    :dep_var
Treated obs:   10253  Est. Type:   :static
Donor obs:     20747  R² First Stage:0.777
Cluster:       :unit
==========================================
        Coef.  Std. Error      t  Pr(>|t|)
──────────────────────────────────────────
_ATT  2.26295   0.0313968  72.08    <1e-99
==========================================

Dynamic event study:

fit_bjs_dynamic(df; y=:dep_var, id=:unit, t=:year, g=:g,
                                      horizons=true, pretrends=true, cluster=:unit)
┌ Warning: Collinear pre-trends omitted: pre20
└ @ Main c:\Users\eohneberg\OneDrive\Julia\Packages\DiDModels.jl\src\estimators\bjs.jl:492
                    BJSModel
================================================
Number of obs:    31000  Outcome:       :dep_var
Treated obs:      10253  Est. Type:     :dynamic
Donor obs:        20747  R² First Stage:   0.777
Cluster:          :unit
================================================
              Coef.  Std. Error      t  Pr(>|t|)
────────────────────────────────────────────────
τ::-19   0.00494698   0.142763    0.03    0.9724
τ::-18  -0.094598     0.142099   -0.67    0.5057
τ::-17  -0.0812596    0.144141   -0.56    0.5731
τ::-16  -0.112528     0.13729    -0.82    0.4126
τ::-15  -0.0753304    0.14184    -0.53    0.5955
τ::-14   0.05654      0.144027    0.39    0.6947
τ::-13  -0.068671     0.143188   -0.48    0.6316
τ::-12  -0.0915126    0.14232    -0.64    0.5204
τ::-11  -0.243579     0.142098   -1.71    0.0868
τ::-10  -0.077792     0.106634   -0.73    0.4659
τ::-9   -0.052073     0.129615   -0.40    0.6880
τ::-8   -0.123991     0.128756   -0.96    0.3358
τ::-7   -0.00807412   0.129888   -0.06    0.9504
τ::-6   -0.242521     0.131568   -1.84    0.0656
τ::-5   -0.157734     0.130259   -1.21    0.2262
τ::-4   -0.0704127    0.126895   -0.55    0.5791
τ::-3   -0.105707     0.132233   -0.80    0.4242
τ::-2   -0.0469036    0.13182    -0.36    0.7221
τ::-1   -0.11832      0.128169   -0.92    0.3561
τ::0     1.51314      0.0754774  20.05    <1e-74
τ::1     1.66384      0.0767514  21.68    <1e-84
τ::2     1.86437      0.0745015  25.02    <1e-99
τ::3     1.91872      0.074717   25.68    <1e-99
τ::4     1.87322      0.0741817  25.25    <1e-99
τ::5     1.87845      0.0756719  24.82    <1e-99
τ::6     2.14373      0.0763269  28.09    <1e-99
τ::7     2.23778      0.0761084  29.40    <1e-99
τ::8     2.3365       0.0744627  31.38    <1e-99
τ::9     2.34353      0.0747168  31.37    <1e-99
τ::10    2.53443      0.0810955  31.25    <1e-99
τ::11    2.47945      0.119535   20.74    <1e-79
τ::12    2.63494      0.115318   22.85    <1e-92
τ::13    2.9445       0.110473   26.65    <1e-99
τ::14    2.78171      0.114664   24.26    <1e-99
τ::15    2.71471      0.120305   22.57    <1e-90
τ::16    2.88065      0.115632   24.91    <1e-99
τ::17    2.99384      0.114385   26.17    <1e-99
τ::18    2.64617      0.115458   22.92    <1e-93
τ::19    2.87531      0.114058   25.21    <1e-99
τ::20    2.90466      0.113202   25.66    <1e-99
================================================

General specification:

julia> model = fit_bjs(df; 
    y=:dep_var,                   # Outcome variable
    id=:unit,                     # Unit identifier  
    t=:year,                      # Time variable
    g=:g,                         # Treatment timing
    controls=Symbol[],            # Control variables
    fe=(:unit, :year),                   # Fixed effects (default: unit + time)
    weights=:w,                   # Observation weights
    cluster=:unit,                # Clustering variable
    horizons=[0,1,2,3,4],         # Post-treatment periods
    pretrends=[-3,-2,-1],         # Pre-treatment periods
)
                   BJSModel
===============================================
Number of obs:   31000   Outcome:      :dep_var
Treated obs:     10253   Est. Type:    :dynamic
Donor obs:       20747   R² First Stage:  0.780
Cluster:         :unit
===============================================
             Coef.  Std. Error      t  Pr(>|t|)
───────────────────────────────────────────────
τ::-3  -0.00691821   0.0829011  -0.08    0.9335
τ::-2   0.0415102    0.0828488   0.50    0.6165
τ::-1   0.0295101    0.0802191   0.37    0.7130
τ::0    1.53155      0.0828972  18.48    <1e-65
τ::1    1.61755      0.0845701  19.13    <1e-69
τ::2    1.86139      0.0822017  22.64    <1e-91
τ::3    1.97443      0.0811454  24.33    <1e-99
τ::4    1.82614      0.079427   22.99    <1e-93
===============================================

Treatment effect heterogeneity

Sometimes you expect the treatment effect to differ along some dimension. There are two ways to look at this, both matching the Stata did_imputation options of the same name.

Use project when the dimension is continuous and you want to know how the effect changes with it. It regresses the individual imputed effects on the covariates you pass and reports an intercept (τ_cons) together with a slope for each covariate (τ_<var>). A positive, significant slope means the effect is larger when that variable is larger. With horizons you get one intercept and slope per event time.

# Does the effect grow with x1?
fit_bjs(df; y=:dep_var, id=:unit, t=:year, g=:g, project=[:x1], cluster=:unit)

Use hetby when the dimension is discrete (up to 30 groups). It reports a separate effect for each group value, named τ_<value>, or τ<h>_<value> when combined with horizons.

# A separate ATT for each region
fit_bjs(df; y=:dep_var, id=:unit, t=:year, g=:g, hetby=:region, cluster=:unit)

project and hetby cannot be used together.

Standard errors

The standard errors on project slopes and hetby group effects are not naive OLS errors on the imputed effects. They use the same clustered influence function variance that the package applies to every other treatment coefficient, and they honor the cluster argument. So for project the slope inference is clustered at the level you pass, and you do not need to cluster or bootstrap it by hand. The usual caveat applies: the errors are only as reliable as the number of clusters, so be careful with very few clusters.

Suppressing thin coefficients with `minn`

minn sets the minimum effective sample size a coefficient needs before it is reported. It defaults to 0, which keeps every coefficient, so existing code is unaffected. Setting minn=30 reproduces Stata's default behaviour and drops coefficients (often long horizons or small heterogeneity cells) that rest on too few effective observations, since their standard errors can be misleadingly small. Suppressed coefficients are reported in a warning.

fit_bjs(df; y=:dep_var, id=:unit, t=:year, g=:g, horizons=true, minn=30, cluster=:unit)

Gardner Two-Stage DiD

Static:

julia> fit_gardner_static(df; y=:dep_var, id=:unit, t=:year, g=:g,
                                cluster=:unit)
               GardnerModel
==========================================
Number of obs: 31000  R²:            0.353
Treated obs:   10253  R² adjusted:   0.353
Donor obs:     20747  F-statistic: 16904.0
Outcome:    :dep_var  Cluster :      :unit
==========================================
        Coef.  Std. Error      t  Pr(>|t|)
──────────────────────────────────────────
_ATT  2.26295   0.0338789  66.80    <1e-99
==========================================

Dynamic:

julia> fit_gardner_dynamic(df; y=:dep_var, id=:unit, t=:year, g=:g,
                                 cluster=:unit, ref_p=-1)
                  GardnerModel
=================================================
Number of obs:    31000   R²:               0.373
Treated obs:      10253   R² adjusted:      0.372
Donor obs:        20747   F-statistic:    460.171
Outcome:       :dep_var   Cluster :         :unit
=================================================
               Coef.  Std. Error      t  Pr(>|t|)
─────────────────────────────────────────────────
τ::-20   0.0482675     0.0604799   0.80    0.4248
τ::-19   0.0434281     0.0607702   0.71    0.4748
τ::-18  -0.000894858   0.0609516  -0.01    0.9883
τ::-17  -0.028753      0.0632599  -0.45    0.6495
τ::-16   0.0246948     0.0602951   0.41    0.6821
τ::-15   0.0228411     0.0612728   0.37    0.7093
τ::-14   0.0840854     0.064303    1.31    0.1910
τ::-13   0.0107916     0.0615972   0.18    0.8609
τ::-12  -0.023326      0.0637062  -0.37    0.7143
τ::-11  -0.10326       0.0639668  -1.61    0.1065
τ::-10  -0.00170496    0.0413833  -0.04    0.9671
τ::-9    0.0129794     0.0411607   0.32    0.7525
τ::-8   -0.0143057     0.0416646  -0.34    0.7313
τ::-7    0.0533169     0.0409899   1.30    0.1934
τ::-6   -0.082763      0.0412686  -2.01    0.0449
τ::-5   -0.0377117     0.0413368  -0.91    0.3616
τ::-4   -0.00635132    0.0407193  -0.16    0.8761
τ::-3   -0.00746851    0.0414751  -0.18    0.8571
τ::-2    0.0315947     0.0421007   0.75    0.4530
τ::0     1.51314       0.076538   19.77    <1e-85
τ::1     1.66384       0.0782553  21.26    <1e-98
τ::2     1.86437       0.0761971  24.47    <1e-99
τ::3     1.91872       0.0759098  25.28    <1e-99
τ::4     1.87322       0.0750175  24.97    <1e-99
τ::5     1.87845       0.0765166  24.55    <1e-99
τ::6     2.14373       0.0763666  28.07    <1e-99
τ::7     2.23778       0.0763247  29.32    <1e-99
τ::8     2.3365        0.0749124  31.19    <1e-99
τ::9     2.34353       0.0747161  31.37    <1e-99
τ::10    2.53443       0.0811948  31.21    <1e-99
τ::11    2.47945       0.119535   20.74    <1e-94
τ::12    2.63494       0.115318   22.85    <1e-99
τ::13    2.9445        0.110473   26.65    <1e-99
τ::14    2.78171       0.114664   24.26    <1e-99
τ::15    2.71471       0.120305   22.57    <1e-99
τ::16    2.88065       0.115632   24.91    <1e-99
τ::17    2.99384       0.114385   26.17    <1e-99
τ::18    2.64617       0.115458   22.92    <1e-99
τ::19    2.87531       0.114058   25.21    <1e-99
τ::20    2.90466       0.113202   25.66    <1e-99
=================================================

Sun-Abraham Estimator

ATT:

julia> fit_sunab(df; y=:dep_var, id=:unit, t=:year, g=:g,
                         cluster=:unit, agg=:att)
               SunabModel
=========================================
Number of obs:31000   R²:           0.799
Treated obs:  10253   R² adjusted:  0.792
Pre obs:      20747   F-statistic:1.23502e5
Dep. Var:  :dep_var   Cluster:      :unit
=========================================
       Coef.  Std. Error      t  Pr(>|t|)
─────────────────────────────────────────
ATT  2.32119   0.0820088  28.30    <1e-99
=========================================

Dynamic coefficients:

julia> fit_sunab(df; y=:dep_var, id=:unit, t=:year, g=:g,
                         cluster=:unit, agg=:dynamic)
                   SunabModel
=================================================
Number of obs:    31000   R²:               0.799
Treated obs:      10253   R² adjusted:      0.792
Pre obs:          20747   F-statistic:    3083.66
Dep. Var:      :dep_var   Cluster:          :unit
=================================================
               Coef.  Std. Error      t  Pr(>|t|)
─────────────────────────────────────────────────
τ::-20  -0.0151877     0.147769   -0.10    0.9182
τ::-19  -0.0082711     0.147491   -0.06    0.9553
τ::-18  -0.0997613     0.152664   -0.65    0.5136
τ::-17  -0.0760892     0.151675   -0.50    0.6160
τ::-16  -0.113553      0.152684   -0.74    0.4572
τ::-15  -0.121286      0.152397   -0.80    0.4263
τ::-14   0.000968567   0.150746    0.01    0.9949
τ::-13  -0.0875309     0.148901   -0.59    0.5568
τ::-12  -0.0281754     0.154189   -0.18    0.8550
τ::-11  -0.324711      0.152768   -2.13    0.0338
τ::-10   0.0398103     0.108042    0.37    0.7126
τ::-9    0.0655079     0.108619    0.60    0.5466
τ::-8   -0.00649778    0.107914   -0.06    0.9520
τ::-7    0.109307      0.108343    1.01    0.3133
τ::-6   -0.125073      0.108768   -1.15    0.2505
τ::-5   -0.039797      0.108363   -0.37    0.7135
τ::-4    0.0476292     0.10436     0.46    0.6482
τ::-3    0.0119353     0.103833    0.11    0.9085
τ::-2    0.0698439     0.109226    0.64    0.5227
τ::0     1.53545       0.109545   14.02    <1e-40
τ::1     1.69124       0.109982   15.38    <1e-47
τ::2     1.87083       0.106399   17.58    <1e-59
τ::3     1.9474        0.108805   17.90    <1e-61
τ::4     1.84971       0.106231   17.41    <1e-58
τ::5     1.89713       0.109857   17.27    <1e-57
τ::6     2.18776       0.107846   20.29    <1e-76
τ::7     2.25533       0.105809   21.32    <1e-82
τ::8     2.32726       0.108476   21.45    <1e-83
τ::9     2.3889        0.0952692  25.08    <1e-99
τ::10    2.55571       0.109845   23.27    <1e-95
τ::11    2.62696       0.161015   16.32    <1e-52
τ::12    2.78245       0.153434   18.13    <1e-63
τ::13    3.09201       0.153139   20.19    <1e-75
τ::14    2.92923       0.152714   19.18    <1e-69
τ::15    2.86222       0.158084   18.11    <1e-62
τ::16    3.02817       0.155266   19.50    <1e-71
τ::17    3.14135       0.152887   20.55    <1e-77
τ::18    2.79368       0.158095   17.67    <1e-60
τ::19    3.02282       0.153328   19.71    <1e-72
τ::20    3.05217       0.152721   19.99    <1e-74
=================================================

Two-Way Fixed Effects

Static:

julia> fit_twfe_static(df; y=:dep_var, id=:unit, t=:year, g=:g,
                               cluster=:unit)
                TWFEModel
==========================================
Number of obs: 31000  R²:            0.794
Post obs:      21320  R² adjusted:   0.787
Pre obs:        9680  F-statistic:119798.0
Dep. Var:   :dep_var  Cluster:       :unit
==========================================
        Coef.  Std. Error      t  Pr(>|t|)
──────────────────────────────────────────
post  2.01215   0.0311817  64.53    <1e-99
==========================================

Dynamic:

julia> fit_twfe_dynamic(df; y=:dep_var, id=:unit, t=:year, g=:g,
                                cluster=:unit, ref_p=-1)
                   TWFEModel
================================================
Number of obs:    31000  R²:               0.799
Post obs:         10253  R² adjusted:      0.792
Pre obs:          20747  F-statistic:    3073.42
Dep. Var:      :dep_var  Cluster:          :unit
================================================
              Coef.  Std. Error      t  Pr(>|t|)
────────────────────────────────────────────────
τ::-20   0.228396      0.121549   1.88    0.0605
τ::-19   0.225683      0.122522   1.84    0.0658
τ::-18   0.124711      0.127555   0.98    0.3285
τ::-17   0.143929      0.127102   1.13    0.2577
τ::-16   0.122706      0.123789   0.99    0.3218
τ::-15   0.159711      0.126671   1.26    0.2077
τ::-14   0.317349      0.126036   2.52    0.0120
τ::-13   0.181699      0.123417   1.47    0.1413
τ::-12   0.148058      0.127864   1.16    0.2472
τ::-11   0.0253997     0.104909   0.24    0.8087
τ::-10  -0.0456146     0.104611  -0.44    0.6629
τ::-9   -0.0361548     0.106546  -0.34    0.7344
τ::-8   -0.111102      0.105043  -1.06    0.2905
τ::-7    0.0172967     0.105776   0.16    0.8701
τ::-6   -0.195828      0.105384  -1.86    0.0634
τ::-5   -0.11145       0.105431  -1.06    0.2907
τ::-4    0.0305665     0.101692   0.30    0.7638
τ::-3   -0.0268863     0.10111   -0.27    0.7904
τ::-2    0.00899443    0.106906   0.08    0.9330
τ::0     1.47408       0.104917  14.05    <1e-40
τ::1     1.63928       0.107153  15.30    <1e-46
τ::2     1.82363       0.103913  17.55    <1e-59
τ::3     1.90212       0.106412  17.87    <1e-61
τ::4     1.79594       0.10399   17.27    <1e-57
τ::5     1.82125       0.107351  16.97    <1e-56
τ::6     2.09319       0.104439  20.04    <1e-74
τ::7     2.18456       0.103711  21.06    <1e-81
τ::8     2.30298       0.106062  21.71    <1e-85
τ::9     2.27728       0.091008  25.02    <1e-99
τ::10    2.55733       0.108288  23.62    <1e-97
τ::11    2.44798       0.136409  17.95    <1e-61
τ::12    2.6095        0.131919  19.78    <1e-73
τ::13    2.89423       0.130662  22.15    <1e-88
τ::14    2.68905       0.127594  21.08    <1e-81
τ::15    2.62285       0.135489  19.36    <1e-70
τ::16    2.68003       0.132898  20.17    <1e-75
τ::17    2.83728       0.128553  22.07    <1e-87
τ::18    2.5352        0.133589  18.98    <1e-68
τ::19    2.6402        0.125345  21.06    <1e-81
τ::20    2.76279       0.129787  21.29    <1e-82
================================================

Model Output

All models support standard StatsAPI functions:

coef(model)              # Coefficient estimates
vcov(model)              # Variance-covariance matrix
stderror(model)          # Standard errors
coefnames(model)         # Coefficient names
confint(model)           # Confidence intervals
nobs(model)              # Number of observations

Post-Estimation Analysis

Cumulative Effects

julia> model = fit_sunab(df; y=:dep_var, id=:unit, t=:year, g=:g,
                                cluster=:unit, agg=:dynamic);
julia> cum_effects = cumulative_effects(model);

# Access results
julia> cum_effects.τ           # Event times
40-element Vector{Int64}:
 -20
 -19
   ⋮
  19
  20

julia> cum_effects.cumulative  # Cumulative effects
40-element Vector{Float64}:
 -0.7009304415811053
 -0.6857427528313305
  ⋮
 48.78563490491884
 51.83780653599853

julia> cum_effects.std_errors  # Standard errors
40-element Vector{Float64}:
 1.6669189569635976
 1.5663756814986112
 ⋮
 1.728900903879408
 1.8318442896860898

Plotting

Simple Event Study Plot

using Makie, CairoMakie
m = fit_bjs_dynamic(df; y=:dep_var, id=:unit, t=:year, g=:g,cluster=:unit);
   fig = plot_event_study(m; title="BJS Event Study", color=:steelblue)

Event Study with Multiple Estimators

result = plot_comparison(df; 
       y=:dep_var, id=:unit, t=:year, g=:g,
       models=[:bjs, :gardner, :sunab, :twfe],
       cluster=:unit).figure

Common Parameters

All estimator functions accept:

df::DataFrame: Panel dataset
y::Symbol: Outcome variable name
id::Symbol: Unit identifier
t::Symbol: Time period variable
g::Symbol: Treatment timing variable
controls::Vector{Symbol}: Additional controls (default: Symbol[])
cluster::Symbol: Clustering variable (default: clusters by id)
weights::Union{Nothing,Symbol}: Observation weights (default: nothing)

fit_bjs additionally accepts:

project::Vector{Symbol}: Continuous treatment effect heterogeneity, the Stata project() option (default: Symbol[])
hetby::Union{Nothing,Symbol}: Discrete treatment effect heterogeneity, the Stata hetby() option (default: nothing)
minn::Real: Minimum effective sample size per coefficient. 0 keeps everything, 30 matches Stata's default (default: 0)

R / Stata Equivalents

StagDiDModels.jl	R	Stata
`fit_bjs`, `fit_bjs_dynamic`, `fit_bjs_static`	`didimputation::did_imputation` †	`did_imputation`
`fit_gardner_dynamic`, `fit_gardner_static`	`did2s::did2s`	—
`fit_sunab`	`fixest::feols(y ~ sunab(g, t) \| id + t)`	—
`fit_twfe_dynamic`, `fit_twfe_static`	`fixest::feols(y ~ i(event_time) \| id + t)`	—

† The R didimputation package reports conservative standard errors (it omits the influence-function variance) and lacks the hetby / project options; StagDiDModels follows the Stata did_imputation implementation. See Performance.

Performance

All four estimators are pure Julia and were benchmarked against the standard reference implementations on synthetic staggered-adoption panels. Timings are warm (compilation excluded); reference tools and the BJS "single-threaded" row run on one thread for fairness, on an Intel Core Ultra 9 185H (Julia 1.11, R 4.5.1, StataNow 19 SE). Across all four estimators, coefficients and standard errors reproduce the reference implementations to ~7–8 significant figures — down to floating-point differences between regression backends.

120,000 observations (10,000 units × 12 periods), dynamic event study with clustered standard errors:

Estimator	Reference	Reference	StagDiDModels	Speed-up
BJS imputation — single-threaded	Stata `did_imputation`	12.5 s	1.30 s	9.6×
BJS imputation — multithreaded (16 cores)	Stata `did_imputation`	12.5 s	0.93 s	13.4×
Gardner two-stage	R `did2s`	18.4 s	4.02 s	4.6×
Sun–Abraham	R `fixest::sunab`	0.16 s	0.12 s	1.3×
TWFE event study	R `fixest::feols`	0.04 s	0.08 s	0.5×

Notes:

BJS is ~9–11× faster than Stata did_imputation single-threaded and ~12–15× with the multithreaded path (multithreaded=true, the default), roughly flat across 12k–120k obs. Stata SE is single-threaded; even granting Stata MP an optimistic 2–3× the lead holds.
Gardner speed-up widens on smaller panels — ~16× at 12k obs, narrowing to 4.6× at 120k because its influence-function SE solve has fixed-effect fill-in that grows with the number of units. (did2s scales worse still.)
Sun–Abraham is a modest win (~1.3× at 120k, ~2.5× at 12k); it's a single saturated regression plus a cheap aggregation, so it's backend-bound.
TWFE is effectively a tie: fixest's compiled core is marginally faster on a plain event-study feols. The value here is the unified API and matching output, not raw speed.
The R didimputation package runs the BJS estimator faster than Stata, but it reports conservative standard errors (it omits the influence-function variance) and lacks the hetby / project heterogeneity options. StagDiDModels follows the Stata did_imputation implementation — which it reproduces exactly — so didimputation is not a like-for-like comparison and is not shown.

Benchmark harnesses are in bench/.

References

Borusyak, K., Jaravel, X., & Spiess, J. (2023). Revisiting event study designs: Robust and efficient estimation.
Gardner, J. (2022). Two-stage differences in differences.
Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199.

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.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
Project.toml		Project.toml
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StagDiDModels.jl

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Suppressing thin coefficients with minn

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