pyfixest 0.10.11.0

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 clone of the excellent 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:
  • Gardner's two-stage ("Did2s") estimator is available via the pyfixest.experimental.did module

News

PyFixest 0.10.8 adds experimental support for Gardner's two stage "DID2s" estimator:

import pandas as pd
import numpy as np
from pyfixest.experimental.did import did2s
from pyfixest.estimation import feols
from pyfixest.visualize import iplot

# download csv from this repo
df_het = pd.read_csv("https://raw.githubusercontent.com/s3alfisc/pyfixest/master/pyfixest/experimental/data/df_het.csv")

fit = did2s(
    df_het,
    yname = "dep_var",
    first_stage = "~ 0 | state + year",
    second_stage = "~i(rel_year)",
    treatment = "treat",
    cluster = "state",
    i_ref1 = [-1.0, np.inf],
)

fit_twfe = feols(
    "dep_var ~ i(rel_year) | state + year",
    df_het,
    i_ref1 = [-1.0, np.inf]
)

iplot([fit, fit_twfe], coord_flip=False, figsize = (900, 400), title = "TWFE vs DID2S")

Installation

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

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 |
# ---