pyfixest 0.13.0

Fast high dimensional fixed effect estimation following syntax of the fixest R package. Supports OLS, IV and Poisson regression and a range of inference procedures. Additionally, experimentally supports (some of) the regression based new Difference-in-Differences Estimators (Did2s).

PyFixest

PyFixest is a Python implementation of the formidable fixest package. The package aims to mimic fixest syntax and functionality as closely as Python allows. 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)
• Difference-in-Difference Estimators:
  • The canonical Two-Way Fixed Effects Estimator
  • Gardner's two-stage ("Did2s") estimator is available via the pyfixest.did.did module
  • Basic Versions of the Local Projections estimator following Dube et al (2023)

Installation

You can install the release version from PyPi by running

pip install pyfixest

or the development version from github by running

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

News

PyFixest 0.14 adds support for the local projections "DID2s" estimator:

import pandas as pd
import numpy as np
from pyfixest.did.lpdid import lpdid

df_het = pd.read_stata("pyfixest/did/data/lpdidtestdata1.dta")
df_het = df_het.astype(np.float64)

fit_lpdid = lpdid(
    df_het,
    yname="Y",
    idname="unit",
    tname="time",
    gname="event_date",
    att=True,
    pre_window=5,
    post_window=10,
)

fit_lpdid.tidy()
#            Estimate	Std. Error	t value	   Pr(>|t|)	    2.5 %	97.5 %	      N
#treat_diff	 31.794381	0.755459	42.086191	0.0	    30.312812	33.27595	28709.0

fit_lpdid = lpdid(
    df_het,
    yname="Y",
    idname="unit",
    tname="time",
    gname="event_date",
    att=False,
    pre_window=5,
    post_window=10,
)

fit_lpdid.iplot(figsize = [1000, 400], xintercept = 5.5, yintercept = 0)

Benchmarks

All benchmarks follow the fixest benchmarks. All non-pyfixest timings are taken from the fixest benchmarks.

Quickstart

For more details, take a look at the tutorial.

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 |
# ---