pyfixest 0.10.6

(Not so) experimental draft package for high dimensional fixed effect estimation. Supports OLS, IV and Poisson regression and a range of inference procedures.

PyFixest

This is a draft package (no longer highly experimental) for a Python clone of the excellent fixest package. The package aims to mimic fixest syntax and functionality as closely as possible. Fixed effects are projected out via the PyHDFE package. For a quick introduction, see the tutorial.

Functionality

At the moment, PyFixest supports

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

Installation

You can install the release version from PyPi by running pip install pyfixest or the development version from github.

News

PyFixest now supports Poisson regression!

from pyfixest.estimation import fepois
from pyfixest.utils import get_data

pdata = get_data(model = "Fepois")
fitpois = fepois("Y~X1 | f1 + f2", vcov = {'CRV1':'group_id'}, data = pdata)

fitpois.summary()

# Model:  Y~X1|f1+f2
# ###
#
# Model:  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            |     -0.019 |        0.050 |    -0.370 |      0.711 |  -0.117 |    0.080 |
# ---
# Deviance: 1067.853

Quickstart

from pyfixest.estimation import feols
from pyfixest.utils import get_data

data = get_data()

# OLS Estimation
fit = feols("Y~X1 | csw0(f1, f2)", data = data, vcov = {'CRV1':'group_id'})
fit.summary()

# ###
#
# Model:  OLS
# Dep. var.:  Y
# Inference:  CRV1
# Observations:  998
#
# | Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
# | Intercept     |      2.206 |        0.078 |    28.304 |      0.000 |   2.043 |    2.370 |
# | X1            |      0.358 |        0.051 |     6.962 |      0.000 |   0.250 |    0.466 |
# ---
# RMSE: 1.765  Adj. R2: 0.024  Adj. R2 Within: 0.024
# ###
#
# Model:  OLS
# Dep. var.:  Y
# Fixed effects:  f1
# Inference:  CRV1
# Observations:  997
#
# | Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
# | X1            |      0.411 |        0.040 |    10.188 |      0.000 |   0.326 |    0.495 |
# ---
# RMSE: 1.421  Adj. R2: 0.048  Adj. R2 Within: 0.048
# ###
#
# Model:  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            |      0.431 |        0.035 |    12.319 |      0.000 |   0.358 |    0.505 |
# ---
# RMSE: 1.2  Adj. R2: 0.07  Adj. R2 Within: 0.07

Standard Errors can be adjusted after estimation, "on-the-fly":

fit1 = fit.fetch_model(0)
fit1.vcov("hetero").tidy()
# Model:  Y~X1
# ###
#
# Model:  OLS
# Dep. var.:  Y
# Inference:  hetero
# Observations:  998
#
# | Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
# | Intercept     |      2.206 |        0.088 |    25.180 |      0.000 |   2.034 |    2.378 |
# | X1            |      0.358 |        0.068 |     5.254 |      0.000 |   0.224 |    0.491 |
# ---
# RMSE: 1.765  Adj. R2: 0.024  Adj. R2 Within: 0.024

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

fit_iv = feols("Y ~ 1 | f1 | X1 ~ Z1", data = data)
fit_iv.summary()

# ###
#
# Model:  IV
# Dep. var.:  Y
# Fixed effects:  f1
# Inference:  CRV1
# Observations:  997
#
# | Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
# | X1            |      0.479 |        0.096 |     4.979 |      0.000 |   0.282 |    0.676 |
# ---