PyFixest: Fast High-Dimensional Fixed Effects Regression in Python
PyFixest is a Python implementation of the formidable fixest package for fast high-dimensional fixed effects regression.
The package aims to mimic fixest syntax and functionality as closely as Python allows: if you know fixest well, the goal is that you won't have to read the docs to get started! In particular, this means that all of fixest's defaults are mirrored by PyFixest - currently with only one small exception.
Nevertheless, for a quick introduction, you can take a look at the documentation or the regression chapter of Arthur Turrell's book on Coding for Economists.
For questions on PyFixest, head on over to our PyFixest Discourse forum.
Features
- OLS, WLS and IV Regression
- Poisson Regression following the pplmhdfe algorithm
- Multiple Estimation Syntax
- Several Robust and Cluster Robust Variance-Covariance Estimators
- Wild Cluster Bootstrap Inference (via wildboottest)
- Difference-in-Differences Estimators:
- The canonical Two-Way Fixed Effects Estimator
- Gardner's two-stage
("
Did2s") estimator - Basic Versions of the Local Projections estimator following Dube et al (2023)
- Multiple Hypothesis Corrections following the Procedure by Romano and Wolf and Simultaneous Confidence Intervals using a Multiplier Bootstrap
- Fast Randomization Inference as in the ritest Stata package
- The Causal Cluster Variance Estimator (CCV) following Abadie et al.
Installation
You can install the release version from PyPI by running
# inside an active virtual environment
python -m pip install pyfixestor the development version from github by running
python -m pip install git+https://github.com/py-econometrics/pyfixestBenchmarks
All benchmarks follow the fixest
benchmarks.
All non-pyfixest timings are taken from the fixest benchmarks.
Quickstart
import pyfixest as pf
data = pf.get_data()
pf.feols("Y ~ X1 | f1 + f2", data=data).summary()###
Estimation: OLS
Dep. var.: Y, Fixed effects: f1+f2
Inference: CRV1
Observations: 997
| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5% | 97.5% |
|:--------------|-----------:|-------------:|----------:|-----------:|-------:|--------:|
| X1 | -0.919 | 0.065 | -14.057 | 0.000 | -1.053 | -0.786 |
---
RMSE: 1.441 R2: 0.609 R2 Within: 0.2Multiple Estimation
You can estimate multiple models at once by using multiple estimation syntax:
# OLS Estimation: estimate multiple models at once
fit = pf.feols("Y + Y2 ~X1 | csw0(f1, f2)", data = data, vcov = {'CRV1':'group_id'})
# Print the results
fit.etable() est1 est2 est3 est4 est5 est6
------------ ----------------- ----------------- ----------------- ----------------- ----------------- -----------------
depvar Y Y2 Y Y2 Y Y2
------------------------------------------------------------------------------------------------------------------------------
Intercept 0.919*** (0.121) 1.064*** (0.232)
X1 -1.000*** (0.117) -1.322*** (0.211) -0.949*** (0.087) -1.266*** (0.212) -0.919*** (0.069) -1.228*** (0.194)
------------------------------------------------------------------------------------------------------------------------------
f2 - - - - x x
f1 - - x x x x
------------------------------------------------------------------------------------------------------------------------------
R2 0.123 0.037 0.437 0.115 0.609 0.168
S.E. type by: group_id by: group_id by: group_id by: group_id by: group_id by: group_id
Observations 998 999 997 998 997 998
------------------------------------------------------------------------------------------------------------------------------
Significance levels: * p < 0.05, ** p < 0.01, *** p < 0.001
Format of coefficient cell:
Coefficient (Std. Error)Adjust Standard Errors "on-the-fly"
Standard Errors can be adjusted after estimation, "on-the-fly":
fit1 = fit.fetch_model(0)
fit1.vcov("hetero").summary()Model: Y~X1
###
Estimation: OLS
Dep. var.: Y
Inference: hetero
Observations: 998
| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5% | 97.5% |
|:--------------|-----------:|-------------:|----------:|-----------:|-------:|--------:|
| Intercept | 0.919 | 0.112 | 8.223 | 0.000 | 0.699 | 1.138 |
| X1 | -1.000 | 0.082 | -12.134 | 0.000 | -1.162 | -0.838 |
---
RMSE: 2.158 R2: 0.123Poisson Regression via fepois()
You can estimate Poisson Regressions via the fepois() function:
poisson_data = pf.get_data(model = "Fepois")
pf.fepois("Y ~ X1 + X2 | f1 + f2", data = poisson_data).summary()###
Estimation: Poisson
Dep. var.: Y, Fixed effects: f1+f2
Inference: CRV1
Observations: 997
| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5% | 97.5% |
|:--------------|-----------:|-------------:|----------:|-----------:|-------:|--------:|
| X1 | -0.007 | 0.035 | -0.190 | 0.850 | -0.075 | 0.062 |
| X2 | -0.015 | 0.010 | -1.449 | 0.147 | -0.035 | 0.005 |
---
Deviance: 1068.169IV Estimation via three-part formulas
Last, PyFixest also supports IV estimation via three part formula
syntax:
fit_iv = pf.feols("Y ~ 1 | f1 | X1 ~ Z1", data = data)
fit_iv.summary()###
Estimation: IV
Dep. var.: Y, Fixed effects: f1
Inference: CRV1
Observations: 997
| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5% | 97.5% |
|:--------------|-----------:|-------------:|----------:|-----------:|-------:|--------:|
| X1 | -1.025 | 0.115 | -8.930 | 0.000 | -1.259 | -0.790 |
---Call for Contributions
Thanks for showing interest in contributing to pyfixest! We appreciate all
contributions and constructive feedback, whether that be reporting bugs, requesting
new features, or suggesting improvements to documentation.
If you'd like to get involved, but are not yet sure how, please feel free to send us an email. Some familiarity with
either Python or econometrics will help, but you really don't need to be a numpy core developer or have published in Econometrica =) We'd be more than happy to invest time to help you get started!
Contributors ✨
Thanks goes to these wonderful people:
This project follows the all-contributors specification. Contributions of any kind welcome!