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 tutorial or the regression chapter of Arthur Turrell's book on Coding for Economists.

Features

• OLS and IV Regression
• Poisson Regression
• Multiple Estimation Syntax
• Several Robust and Cluster Robust Variance-Covariance Types
• Wild Cluster Bootstrap Inference (via wildboottest)
• Difference-in-Difference 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)

Installation

You can install the release version from PyPi by running

pip install pyfixest

or the development version from github by running

pip install git+https://github.com/s3alfisc/pyfixest.git

News

PyFixest 0.13 adds support for the local projections Difference-in-Differences Estimator.

Benchmarks

All benchmarks follow the fixest benchmarks. All non-pyfixest timings are taken from the fixest benchmarks.

Quickstart

Fixed Effects Regression via feols()

You can estimate a linear regression models just as you would in fixest - via feols():

from pyfixest.estimation import feols, fepois
from pyfixest.utils import get_data
from pyfixest.summarize import etable
data = get_data()
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 = feols("Y + Y2 ~X1 | csw0(f1, f2)", data = data, vcov = {'CRV1':'group_id'})
# Print the results
etable([fit.fetch_model(i) for i in range(6)])

Model:  Y~X1
Model:  Y2~X1
Model:  Y~X1|f1
Model:  Y2~X1|f1
Model:  Y~X1|f1+f2
Model:  Y2~X1|f1+f2
                          est1               est2               est3               est4               est5               est6
------------  ----------------  -----------------  -----------------  -----------------  -----------------  -----------------
depvar                       Y                 Y2                  Y                 Y2                  Y                 Y2
-----------------------------------------------------------------------------------------------------------------------------
Intercept     0.919*** (0.121)   1.064*** (0.232)
X1             -1.0*** (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

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 = get_data(model = "Fepois")
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            |     -0.008 |        0.035 |    -0.239 |      0.811 |  -0.076 |    0.060 |
| X2            |     -0.015 |        0.010 |    -1.471 |      0.141 |  -0.035 |    0.005 |
---
Deviance: 1068.836

IV Estimation via three-part formulas

Last, PyFixest also supports IV estimation via three part formula syntax:

fit_iv = 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 |
---