Experimental draft package for high dimensional fixed effect estimation. Supports OLS and IV estimation.
Project description
PyFixest
This is a draft package (highly experimental!) for a Python clone of the excellent fixest package.
Fixed effects are projected out via the PyHDFE package.
For a quick introduction, see the tutorial.
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(X2, X3)", vcov = {'CRV1':'group_id'})
fixest.summary()
# ###
#
# Model: OLS
# Dep. var.: Y
# Inference: {'CRV1': 'group_id'}
# Observations: 1998
#
# Estimate Std. Error t value Pr(>|t|)
# Intercept -3.941395 0.221365 -17.804976 2.442491e-15
# X1 -0.273096 0.165154 -1.653580 1.112355e-01
# ---
# ###
#
# Model: OLS
# Dep. var.: Y
# Fixed effects: X2
# Inference: {'CRV1': 'group_id'}
# Observations: 1998
#
# Estimate Std. Error t value Pr(>|t|)
# X1 -0.260472 0.163874 -1.589472 0.125042
# ---
# ###
#
# Model: OLS
# Dep. var.: Y
# Fixed effects: X2+X3
# Inference: {'CRV1': 'group_id'}
# Observations: 1998
#
# Estimate Std. Error t value Pr(>|t|)
# X1 0.03975 0.107003 0.371481 0.713538
# ---
# IV Estimation
fixest = pf.Fixest(data = data)
fixest.feols("Y~X1 | csw0(X2, X3) | X1 ~ Z1", vcov = {'CRV1':'group_id'})
fixest.summary()
# ###
#
# Model: IV
# Dep. var.: Y
# Inference: {'CRV1': 'group_id'}
# Observations: 1998
#
# Estimate Std. Error t value Pr(>|t|)
# Intercept -3.941293 0.221354 -17.805377 2.442491e-15
# X1 -0.265817 0.261940 -1.014803 3.203217e-01
# ---
# ###
#
# Model: IV
# Dep. var.: Y
# Fixed effects: X2
# Inference: {'CRV1': 'group_id'}
# Observations: 1998
#
# Estimate Std. Error t value Pr(>|t|)
# X1 -0.259964 0.264817 -0.981674 0.336054
# ---
# ###
#
# Model: IV
# Dep. var.: Y
# Fixed effects: X2+X3
# Inference: {'CRV1': 'group_id'}
# Observations: 1998
#
# Estimate Std. Error t value Pr(>|t|)
# X1 0.041142 0.201983 0.203688 0.840315
# ---
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