From great-econometrics
Econometrics skill for Difference-in-Differences (DID) analysis. Activates when the user asks about: "difference in differences", "DID", "DiD", "diff-in-diff", "parallel trends", "treatment group", "control group", "pre-treatment", "post-treatment", "policy evaluation", "natural experiment", "staggered DID", "event study regression", "two-way fixed effects DID", "callaway santanna", "sun and abraham", "双重差分", "倍差法", "平行趋势", "处理组", "对照组", "政策评估", "事件研究", "交错DID", "渐进处理"
npx claudepluginhub zhouziyue233/great-econometrics --plugin econometricsThis skill uses the workspace's default tool permissions.
This skill guides complete DID analysis: from assumption validation and model specification to staggered treatment designs and event study regressions. Designed for policy evaluation and natural experiment settings.
Creates isolated Git worktrees for feature branches with prioritized directory selection, gitignore safety checks, auto project setup for Node/Python/Rust/Go, and baseline verification.
Executes implementation plans in current session by dispatching fresh subagents per independent task, with two-stage reviews: spec compliance then code quality.
Dispatches parallel agents to independently tackle 2+ tasks like separate test failures or subsystems without shared state or dependencies.
This skill guides complete DID analysis: from assumption validation and model specification to staggered treatment designs and event study regressions. Designed for policy evaluation and natural experiment settings.
DID compares the change in outcomes for a treatment group before and after treatment to the change for a control group over the same period.
DID Estimator = (Ȳ_treat,post − Ȳ_treat,pre) − (Ȳ_ctrl,post − Ȳ_ctrl,pre)
Key Assumption (Parallel Trends): In the absence of treatment, the treatment group's outcome would have evolved in parallel with the control group.
Y_it = β₀ + β₁·Treat_i + β₂·Post_t + β₃·(Treat_i × Post_t) + ε_it
β₃ = DID estimate (ATT)
# Python — 2×2 DID with TWFE
import statsmodels.formula.api as smf
# Simple 2x2
model = smf.ols('y ~ treat + post + treat_post', data=df).fit(cov_type='HC3')
# TWFE with entity and time FE (preferred)
from linearmodels.panel import PanelOLS
df_panel = df.set_index(['entity_id', 'year'])
twfe = PanelOLS(df_panel['y'], df_panel[['treat_post']],
entity_effects=True, time_effects=True)
result = twfe.fit(cov_type='clustered', cluster_entity=True)
print(result.summary)
# R — TWFE
library(plm); library(lmtest); library(sandwich)
panel_df <- pdata.frame(df, index = c("entity_id", "year"))
twfe <- plm(y ~ treat_post, data = panel_df, model = "within", effect = "twoways")
coeftest(twfe, vcov = vcovHC(twfe, cluster = "group"))
* Stata — TWFE with clustered SE
xtset entity_id year
xtreg y treat_post i.year, fe cluster(entity_id)
* Or equivalently:
reghdfe y treat_post, absorb(entity_id year) cluster(entity_id)
Replace the single treat_post dummy with relative-time dummies to visualize pre-trends:
* Stata — event study
reghdfe y ib(-1).rel_time, absorb(entity_id year) cluster(entity_id)
coefplot, vertical yline(0) xline(0) ///
title("Event Study: Pre/Post Treatment Effects") ///
xlabel(, angle(45))
# R — event study
library(fixest)
es_model <- feols(y ~ i(rel_time, treat, ref = -1) | entity_id + year,
data = df, cluster = ~entity_id)
iplot(es_model, xlab = "Periods relative to treatment")
Interpreting the event study plot:
When units adopt treatment at different times, standard TWFE can be biased (Callaway-Sant'Anna, Sun-Abraham).
# R — Callaway-Sant'Anna estimator (csdid)
library(did)
cs_result <- att_gt(yname = "y",
gname = "cohort_year", # year of first treatment (0 if never treated)
idname = "entity_id",
tname = "year",
xformla = ~x1 + x2,
data = df)
# Aggregate to average ATT
aggte(cs_result, type = "simple") # Overall ATT
aggte(cs_result, type = "dynamic") # Dynamic effects
ggdid(cs_result)
# R — Sun-Abraham (fixest)
library(fixest)
sa_model <- feols(y ~ sunab(cohort_year, year) | entity_id + year,
data = df, cluster = ~entity_id)
iplot(sa_model)
* Stata — Callaway-Sant'Anna (csdid from SSC)
csdid y x1 x2, ivar(entity_id) time(year) gvar(cohort_year)
csdid_plot
See references/did-reference.md for heterogeneous treatment effects, triple-difference models, synthetic control comparison, Borusyak-Jaravel-Spiess imputation estimator, de Chaisemartin-D'Haultfoeuille estimator, and Roth (2022) pre-trends power analysis.