synthdid 0.10.1

Synthdid

sythdid: Synthetic Difference in Difference Estimation

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.

Instalation

pip install synthdid

Usage

Class input Synthdid

• outcome: 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)

Methods:

• .fit(cov_method = ["optimized", "projected"])
• .vcov(method = ["placebo", "bootstrap", "jackknife"], n_reps:int = 50)

Example

California

import matplotlib.pyplot as plt
import numpy as np, pandas as pd

from synthdid.synthdid import Synthdid as sdid
from synthdid.get_data import quota, california_prop99
pd.options.display.float_format = '{:.4f}'.format

Estimations with Standard Variance-Covariance Estimation

california_estimate = sdid(california_prop99(), unit="State", time="Year", treatment="treated", outcome="PacksPerCapita").fit().vcov(method='placebo')
california_estimate.summary().summary2

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	ATT	Std. Err.	t	P>|t|
0	-15.6038	9.6862	-1.6109	0.1072

Estimations without Standard Variance-Covariance Estimation

california_estimate = sdid(california_prop99(), "State", "Year", "treated", "PacksPerCapita").fit()
california_estimate.summary().summary2

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	ATT	Std. Err.	t	P>|t|
0	-15.6038	-	-	-

Plots

To avoid messages from matplotlib, a semicolon ; should be added at the end of the function call.

This way:

• estimate.plot_outcomes();
• estimate.plot_weights();

california_estimate.plot_outcomes();

california_estimate.plot_weights();

Quota

quota_estimate = sdid(quota(), "country", "year", "quota", "womparl").fit()

quota_estimate.vcov().summary().summary2 ## placebo 

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	ATT	Std. Err.	t	P>|t|
0	8.0341	1.8566	4.3272	0.0000

With covariates

quota_cov = quota().dropna(subset="lngdp")
quota_cov_est = sdid(quota_cov, "country", 'year', 'quota', 'womparl', covariates=['lngdp']).fit()
quota_cov_est.summary().summary2

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	ATT	Std. Err.	t	P>|t|
0	8.0490	-	-	-

Covariable method = 'projected'

quota_cov_est.fit(cov_method="projected").summary().summary2

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	ATT	Std. Err.	t	P>|t|
0	8.0590	-	-	-

quota_cov_est.plot_outcomes()

<synthdid.synthdid.Synthdid at 0x2313747f880>

quota_cov_est.plot_weights()

<synthdid.synthdid.Synthdid at 0x2313747f880>