This package implements the synthetic difference-in-differences estimation procedure, along with a range of inference and graphing procedures, following the work of the author. The package draws on R and Julia code for optimization and Stata code for implementation in contexts with staggered adoption over multiple treatment periods (as well as in a single adoption period as in the original code). The package extends the functionality of the original code, allowing for estimation in a wider range of contexts. Overall, this package provides a comprehensive toolkit for researchers interested in using the synthetic difference-in-differences estimator in their work.
pip install synthdidoutcome: Outcome variable (numeric)unit: Unit variable (numeric or string)time: Time variable (numeric)quota: Dummy of treatement, equal to 1 if units are treated, and otherwise 0 (numeric)
.fit(cov_method: Literal["optimized", "projected"] | None): Estimates the ATE, as well as the time and unit weights. Can use covariates and fit them with the method specified incov_method.vcov(method: Literal["placebo", "bootstrap", "jackknife"], n_reps:int = 50): Estimates the standard error of the ATE estimator
from synthdid.get_data import california_prop99, quota
from synthdid.synthdid import Synthdid
from matplotlib import pyplot as plt
import numpy as npdf = california_prop99()
california_sdid = Synthdid(df, "State", "Year", "treated", "PacksPerCapita").fit().vcov().summary()
california_sdid.summary2| ATT | Std. Err. | t | P > |t| | |
|---|---|---|---|---|
| 0 | -15.60383 | 10.789924 | -1.446148 | 0.148136 |
plt.show(california_sdid.plot_outcomes())
plt.show(california_sdid.plot_weights())
california_did = Synthdid(df, "State", "Year", "treated", "PacksPerCapita").fit(synth = True)
plt.show(california_sc.plot_outcomes())
plt.show(california_sc.plot_weights())
california_did = Synthdid(df, "State", "Year", "treated", "PacksPerCapita").fit(did = True)
plt.show(california_did.plot_outcomes())
plt.show(california_did.plot_weights())
df = quota()
fit_model = Synthdid(df, "country", "year", "quota", "womparl").fit().vcov().summary()
fit_model.summary2| ATT | Std. Err. | t | P > |t| | |
|---|---|---|---|---|
| 0 | 8.0341 | 1.684382 | 4.769762 | 0.000002 |
fit_model.att_info| time | att_time | att_wt | N0 | T0 | N1 | T1 | |
|---|---|---|---|---|---|---|---|
| 0 | 2000.0 | 8.388868 | 0.170213 | 110 | 10 | 1 | 16 |
| 1 | 2002.0 | 6.967746 | 0.297872 | 110 | 12 | 2 | 14 |
| 2 | 2003.0 | 13.952256 | 0.276596 | 110 | 13 | 2 | 13 |
| 3 | 2005.0 | -3.450543 | 0.117021 | 110 | 15 | 1 | 11 |
| 4 | 2010.0 | 2.749035 | 0.063830 | 110 | 20 | 1 | 6 |
| 5 | 2012.0 | 21.762715 | 0.042553 | 110 | 22 | 1 | 4 |
| 6 | 2013.0 | -0.820324 | 0.031915 | 110 | 23 | 1 | 3 |
fit_covar_model = Synthdid(df[~df.lngdp.isnull()], "country", "year", "quota", "womparl", covariates = ["lngdp"]).fit().vcov(method = "bootstrap").summary()
fit_covar_model.summary2| ATT | Std. Err. | t | P > |t| | |
|---|---|---|---|---|
| 0 | 8.04901 | 3.395295 | 2.370636 | 0.017757 |
fit_covar_model_projected = Synthdid(df[~df.lngdp.isnull()], "country", "year", "quota", "womparl", covariates = ["lngdp"]).fit(cov_method = "projected").vcov(method = "bootstrap").summary()
fit_covar_model_projected.summary2| ATT | Std. Err. | t | P > |t| | |
|---|---|---|---|---|
| 0 | 8.05903 | 3.428897 | 2.350327 | 0.018757 |
countries_for_excluding = ["Algeria", "Kenya", "Samoa", "Swaziland", "Tanzania"]
se_examples = Synthdid(
df[~df.country.isin(countries_for_excluding)],
"country",
"year",
"quota",
"womparl"
).fit().vcov(method = "bootstrap").summary()
se_examples.summary2| ATT | Std. Err. | t | P > |t| | |
|---|---|---|---|---|
| 0 | 10.33066 | 5.404923 | 1.911343 | 0.055961 |
se_examples.vcov(method = "placebo").summary()
se_examples.summary2| ATT | Std. Err. | t | P > |t| | |
|---|---|---|---|---|
| 0 | 10.33066 | 2.244618 | 4.602413 | 0.000004 |
se_examples.vcov(method = "jackknife").summary()
se_examples.summary2| ATT | Std. Err. | t | P > |t| | |
|---|---|---|---|---|
| 0 | 10.33066 | 6.04213 | 1.709771 | 0.087308 |