pyfixest 0.29.0

Fast high dimensional fixed effect estimation following syntax of the fixest R package.

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.

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 github discussions.

Features

• OLS, WLS and IV Regression with Fixed-Effects Demeaning via Frisch-Waugh-Lovell
• Poisson Regression following the pplmhdfe algorithm
• Multiple Estimation Syntax
• Probit, Logit and Gaussian Family GLMs (currently without fixed effects demeaning, this is WIP)
• 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.
• Regression Decomposition following Gelbach (2016)
• Publication-ready tables with Great Tables or LaTex booktabs

Installation

You can install the release version from PyPI by running

# inside an active virtual environment
python -m pip install pyfixest

or the development version from github by running

python -m pip install git+https://github.com/py-econometrics/pyfixest

For visualization features using the lets-plot backend, install the optional dependency:

python -m pip install pyfixest[plots]

Note that matplotlib is included by default, so you can always use the matplotlib backend for plotting even without installing the optional lets-plot dependency.

Benchmarks

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: 0.2

Multiple 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: 0.123

Poisson 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: 1068.169

IV 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:

styfenschaer 💻	Niall Keleher 🚇 💻	Wenzhi Ding 💻	Apoorva Lal 💻 🐛	Juan Orduz 🚇 💻	Alexander Fischer 💻 🚇	aeturrell ✅ 📖 📣	leostimpfle 💻 🐛
baggiponte 📖	Sanskriti 🚇	Jaehyung 💻	Alex 📖	Hayden Freedman 💻 📖	Aziz Mamatov 💻	rafimikail 💻	Benjamin Knight 💻
Dirk Sliwka 💻 📖	daltonm-bls 🐛	Marc-André 💻 🐛	Kyle F Butts 🔣	Marco Edward Gorelli 👀	Vincent Arel-Bundock 💻	IshwaraHegde97 💻	Tobias Schmidt 📖
escherpf 🐛 💻	Iván Higuera Mendieta 💻	Ádám Vig 💻	Szymon Sacher 💻	AronNemeth 💻	Dmitri Tchebotarev 💻

This project follows the all-contributors specification. Contributions of any kind welcome!