realseqi/pyfixest

Fast High-Dimensional Fixed Effects Regression in Python following fixest-syntax

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 following the ppmlhdfe algorithm
• 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)
• Multiple Hypothesis Corrections following the Procedure by Romano and Wolf

Installation

You can install the release version from PyPi by running

pip install -U pyfixest

or the development version from github by running

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

News

PyFixest 0.15.2 adds support for Romano-Wolf Corrected p-values via the rwolf() function.

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
pf.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
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            |     -0.008 |        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 = 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 |
---