Experimental draft package for high dimensional fixed effect estimation. Supports OLS and IV estimation.
Project description
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!
import pyfixest as pf
from pyfixest.utils import get_poisson_data
pdata = get_poisson_data()
fitpois = fepois("Y~X1 | X2+X3+X4", vcov = {'CRV1':'X4'})
fitpois.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()
# OLS Estimation
fit = feols("Y~X1 | csw0(f1, f2)", vcov = {'CRV1':'group_id'})
fit.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:
fit_iv = feols("Y~ 1 | f2 + f3 | X1 ~ Z1", vcov = {'CRV1':'group_id'})
fit_iv.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":
fit_iv.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 |
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