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.
- 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.
You can install the release version from PyPi by running
pip install -U pyfixestor the development version from github by running
pip install git+https://github.com/py-econometrics/pyfixest.gitAll benchmarks follow the fixest benchmarks. All non-pyfixest timings are taken from the fixest benchmarks.
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: 0.2 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) 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: 0.123 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: 1068.169 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 | --- 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!
Thanks goes to these wonderful people:
 styfenschaer 💻 |  Niall Keleher 🚇 💻 |  Wenzhi Ding 💻 |  Apoorva Lal 💻 🐛 |  Juan Orduz 🚇 💻 |  Alexander Fischer 💻 🚇 |  aeturrell ✅ 📖 📣 |
 leostimpfle 💻 |
This project follows the all-contributors specification. Contributions of any kind welcome!