StataFlow 0.1.4

StataFlow: A Python econometrics toolkit aligned with Stata 17

StataFlow

StataFlow (stataflow) is a Python econometrics toolkit that reproduces Stata 17 estimation results with high precision. It provides both a Stata-compatible command layer (for researchers migrating from Stata) and a native Python estimator layer (for advanced users who want direct control).

What you can do today

• Run Stata-style commands in Python: regress, reghdfe, ivregress 2sls, logit, ppmlhdfe, did_imputation, csdid, rdrobust, and more.
• Obtain coefficients, standard errors, t/z-statistics, p-values, and confidence intervals that are field-level verified against Stata 17.
• Work with high-dimensional fixed effects (HDFE), IV/2SLS, binary/count models, and DID/event-study estimators.
• Use Stata-style factor-variable syntax (i.group##c.post, c.x1#c.x2, x1##x2) and space-separated absorb strings directly in wrapper commands. Bare variables inside # / ## are treated as continuous, matching common Stata usage.

What is not yet supported

• Multi-way clustering — only single-cluster robust inference is available.
• Direct post-estimation on wrapper returns — the compat.stata wrappers return ResultSchema result objects. predict and margins are available on the core estimator layer only.
• Full command surfaces for community commands — reghdfe, ivreghdfe, ppmlhdfe, did_imputation, eventstudyinteract, csdid, and rdrobust are implemented as verified high-frequency subsets, not complete Stata command reproductions. Unsupported options are explicitly rejected rather than silently ignored.

Completeness legend

• Stable — synthetic + real-data dual-run verified; core API is unlikely to change.
• Alpha — high-frequency paths are implemented and verified, but the command surface is still a subset of the full Stata community command.
• Alpha — Partial — a verifiable implementation exists, but large functional areas are still missing (e.g., fuzzy RD for rdrobust, multi-way clustering).

See the Command Support Matrix for the per-command detailed status.

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Installation

pip install StataFlow

Requirements: Python 3.10+, NumPy, pandas, SciPy.

For development (editable install from source):

git clone https://github.com/ZhenHaoFu810/StataFlow.git
cd StataFlow
pip install -e .

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Quick start

Stata-compatible command layer (recommended)

All compat.stata wrappers return a ResultSchema object with coefficients, standard errors, and fit statistics. They do not expose .predict() or .margins() directly—use the core estimator layer below for post-estimation.

import pandas as pd
from stataflow.compat.stata import regress, reghdfe, ivregress_2sls, logit

# OLS with robust standard errors
result = regress(df, y="wage", x=["edu", "exper"], vce="robust")

# High-dimensional fixed effects (reghdfe)
result = reghdfe(
    df, y="wage", x=["edu", "exper"],
    absorb="firm_id year_id", vce="cluster", cluster="industry"
)

# Factor-variable syntax in HDFE
result = reghdfe(
    df, y="wage", x=["i.industry##c.post"], absorb="firm_id year_id"
)

# 2SLS
result = ivregress_2sls(
    df, y="lwage", x_exog=["edu"], x_endog=["exper"],
    instruments=["age", "kidslt6"], vce="robust"
)

# Logit
result = logit(df, y="inlf", x=["nwifeinc", "educ", "exper"])

For runnable examples, see the examples/ directory:

• examples/demo_regress.py
• examples/demo_reghdfe.py
• examples/demo_ppmlhdfe.py
• examples/demo_ivregress_2sls.py

Native Python estimator layer (advanced)

from stataflow import OLS, FixedEffectsOLS, AbsorbingOLS, Logit, IV2SLS

model = OLS(data=df, y="wage", x=["edu", "exper"])
result = model.fit(vce="robust")

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Supported commands

Command	Python entry	Core capabilities
regress	stataflow.compat.stata.regress	OLS, robust, cluster, aweight
xtreg, fe	stataflow.compat.stata.xtreg_fe	Fixed effects (within), cluster
areg	stataflow.compat.stata.areg	Single absorb variable FE
reghdfe	stataflow.compat.stata.reghdfe	1-2 group HDFE, cluster, singleton drop
ivregress 2sls	stataflow.compat.stata.ivregress_2sls	2SLS, robust, cluster
ivreghdfe	stataflow.compat.stata.ivreghdfe	IV + 1-2 group HDFE, cluster
logit	stataflow.compat.stata.logit	MLE, robust, cluster
probit	stataflow.compat.stata.probit	MLE, robust, cluster
poisson	stataflow.compat.stata.poisson	MLE, robust, cluster
ppmlhdfe	stataflow.compat.stata.ppmlhdfe	PPML + 1-2 group HDFE
did_imputation	stataflow.compat.stata.did_imputation	BJS DID imputation
eventstudyinteract	stataflow.compat.stata.eventstudyinteract	Sun & Abraham IW estimator
csdid	stataflow.compat.stata.csdid	Callaway-Sant'Anna DID (method="reg" only)
rdrobust	stataflow.compat.stata.rdrobust	Sharp RD local polynomial (bwselect="mserd", covs)

Full details: docs/command-support-matrix/README.md

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Validation philosophy

Every public command is validated with two lines of evidence:

1. Synthetic / controlled cases — formula, degrees of freedom, sample screening, edge cases.
2. Real public datasets — field-level comparison against Stata 17 on openly available economic/financial data.

A command is considered "done" only when both lines pass and the source-to-Python mapping is documented. We do not accept "statistical equivalence" without explicit mathematical or source-code justification.

Public evidence and results are available in research/results/validation/.

Running tests

# Unit and integration tests (fast)
pytest tests/ -v --ignore=tests/golden/

# Golden dual-run tests (require Stata 17)
pytest tests/golden/ -v

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Project structure

• src/stataflow/estimators/ — Core Python estimators (OLS, AbsorbingOLS, Logit, PPMLHDFE, DIDImputation, etc.)
• src/stataflow/compat/stata/ — Stata command wrappers (regress(), reghdfe(), ivregress_2sls(), etc.)
• docs/command-support-matrix/ — Per-command support matrices
• examples/ — Runnable demonstration scripts
• tests/ — Unit and integration tests

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Default target version

Stata 17

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Documentation

• User guide
• Chinese user guide
• Cookbook
• Chinese cookbook
• Command support matrices

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Governance

• Codex — project goals, architecture, review gates, and statistical-dispute arbitration.
• Claude Code — implementation, testing, and evidence backfill.