pyfixest 0.8.9.0

Experimental draft package for high dimensional fixed effect estimation. Supports OLS and IV estimation.

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.

Installation

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

News

The dev version of PyFixest (v0.8.4) now supports Poisson regression!

import pyfixest as pf
from pyfixest.utils import get_poisson_data

pdata = get_poisson_data()
fixest = pf.Fixest(data = pdata)
fixest.fepois("Y~X1 | X2+X3+X4", vcov = {'CRV1':'X4'})

fixest.summary()
# Model:  Poisson
# Dep. var.:  Y
# Fixed effects:  X2+X3+X4
# Inference:  {'CRV1': 'X4'}
# Observations:  1000

# | Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
# | X1            |      0.874 |        0.037 |    23.780 |      0.000 |   0.802 |    0.946 |
# ---
# Deviance: 481157.824

Quickstart

import pyfixest as pf
import numpy as np
from pyfixest.utils import get_data

data = get_data()

fixest = pf.Fixest(data = data)
# OLS Estimation
fixest.feols("Y~X1 | csw0(f1, f2)", vcov = {'CRV1':'group_id'})
fixest.summary()
# ###
#
# Model:  OLS
# Dep. var.:  Y
# Inference:  {'CRV1': 'group_id'}
# Observations:  998
#
# | Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
# | Intercept     |      2.204 |        0.054 |    40.495 |      0.000 |   2.096 |    2.312 |
# | X1            |      0.351 |        0.063 |     5.595 |      0.000 |   0.227 |    0.476 |
# ---
# RMSE: 1.751  Adj. R2: 0.037  Adj. R2 Within: 0.037
# ###
#
# Model:  OLS
# Dep. var.:  Y
# Fixed effects:  f1
# Inference:  {'CRV1': 'group_id'}
# Observations:  997
#
# | Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
# | X1            |      0.326 |        0.048 |     6.756 |      0.000 |   0.230 |    0.422 |
# ---
# RMSE: 1.407  Adj. R2: 0.049  Adj. R2 Within: 0.049
# ###
#
# Model:  OLS
# Dep. var.:  Y
# Fixed effects:  f1+f2
# Inference:  {'CRV1': 'group_id'}
# Observations:  997
#
# | Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
# | X1            |      0.355 |        0.039 |     9.044 |      0.000 |   0.277 |    0.433 |
# ---
# RMSE: 1.183  Adj. R2: 0.078  Adj. R2 Within: 0.078

PyFixest also supports IV (Instrumental Variable) Estimation:

fixest = pf.Fixest(data = data)
fixest.feols("Y~ 1 | f2 + f3 | X1 ~ Z1", vcov = {'CRV1':'group_id'})
fixest.summary()
# ###
#
# Model:  IV
# Dep. var.:  Y
# Fixed effects:  f2+f3
# Inference:  {'CRV1': 'group_id'}
# Observations:  998
#
# | Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
# | X1            |      0.309 |        0.058 |     5.306 |      0.000 |   0.193 |    0.424 |
# ---

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

fixest.vcov("hetero").tidy()
# ###
#
# Model:  IV
# Dep. var.:  Y
# Fixed effects:  f2+f3
# Inference:  hetero
# Observations:  998
#
# | Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5 % |   97.5 % |
# |:--------------|-----------:|-------------:|----------:|-----------:|--------:|---------:|
# | X1            |      0.309 |        0.063 |     4.877 |      0.000 |   0.184 |    0.433 |