A set of command line tools to perform econometric analysis.
Background on Model wikipedia
Documentation
usage: diff_in_diff.py [-h] data_file treatment post outcome_variable [confounding_variables ...]
Run difference-in-differences analysis
positional arguments:
data_file Path to the data file
treatment Treatment variable
post Post variable
outcome_variable Outcome variable
confounding_variables
List of confounding variables
options:
-h, --help show this help message and exit
Example
python diff_in_diff.py 'data/kielmc_clean.xlsx' "nearinc" "y81" "lprice" "rooms" "age" "land" "area"
Output
OLS Regression Results
==============================================================================
Dep. Variable: lprice R-squared: 0.751
Model: OLS Adj. R-squared: 0.746
Method: Least Squares F-statistic: 134.6
Date: Thu, 11 Apr 2024 Prob (F-statistic): 2.52e-90
Time: 12:10:41 Log-Likelihood: 32.968
No. Observations: 320 AIC: -49.94
Df Residuals: 312 BIC: -19.79
Df Model: 7
Covariance Type: nonrobust
==================================================================================
coef std err t P>|t| [0.025 0.975]
----------------------------------------------------------------------------------
const 10.0705 0.105 96.053 0.000 9.864 10.277
nearinc -0.0692 0.041 -1.697 0.091 -0.149 0.011
y81 0.3861 0.031 12.548 0.000 0.326 0.447
treatment_post -0.0817 0.055 -1.478 0.140 -0.190 0.027
rooms 0.0989 0.017 5.709 0.000 0.065 0.133
age -0.0038 0.000 -9.349 0.000 -0.005 -0.003
land 8.964e-07 3.33e-07 2.691 0.008 2.41e-07 1.55e-06
area 0.0003 2.18e-05 11.989 0.000 0.000 0.000
==============================================================================
Omnibus: 53.069 Durbin-Watson: 1.731
Prob(Omnibus): 0.000 Jarque-Bera (JB): 189.391
Skew: -0.668 Prob(JB): 7.49e-42
Kurtosis: 6.524 Cond. No. 4.92e+05
==============================================================================