pyfixest Skill

pyfixest: fast high-dimensional fixed effects estimation for Python. Covers OLS, Poisson, and IV regression with multi-way fixed effects; difference-in-differences estimators (TWFE, did2s, lpdid, Sun-Abraham); clustered standard errors; wild bootstrap; and publication output (etable regression tables, coefplot, iplot event study plots). Use when running fixed effects regressions, difference-in-differences designs, Poisson count models with FE, or producing publication-ready regression tables. For panel random/between effects, use linearmodels; for GLM/time series without FE, use statsmodels.

Comprehensive skill for fixed effects regression, instrumental variables, and difference-in-differences estimation with pyfixest. Use decision trees below to find the right guidance, then load detailed references.

What is pyfixest?

pyfixest is a Python implementation of the R fixest package (Berge, Butts, & McDermott, 2026):

• Fast: Multi-way FE demeaning via alternating projections with numba/JAX/GPU backends
• Concise formula syntax: Fixed effects after |, IV after second |, multiple estimation via sw()/csw()
• Modern DiD: Built-in did2s, local projections DiD (lpdid), and Sun-Abraham saturated estimator
• Flexible inference: Switch SE types post-estimation; wild bootstrap, randomization inference, CCV
• Publication output: etable() for regression tables, coefplot() and iplot() for coefficient visualization

Version Notes

This skill targets pyfixest 0.40.0, the major release aligning with R fixest 0.13. Breaking changes from earlier versions:

• Default standard errors changed from "cluster by first FE" to "iid" — old code silently produces different SEs
• ssc() arguments renamed: adj → k_adj, fixef_k → k_fixef, cluster_adj → G_adj, cluster_df → G_df
• fixef_rm default changed from "none" to "singleton" — singletons now dropped by default
• Multicollinearity tolerance reduced from 1e-10 to 1e-09

How to Use This Skill

Reference File Structure

Each topic in ./references/ contains focused documentation:

| File | Purpose | When to Read | |------|---------|--------------| | quickstart.md | Installation, first regression, formula syntax | Starting with pyfixest | | fixed-effects.md | Multi-way FE, SE types, clustering, wild bootstrap | FE models and inference | | instrumental-variables.md | IV syntax, first stage, weak instruments | IV/2SLS estimation | | difference-in-differences.md | TWFE, did2s, lpdid, Sun-Abraham, event studies | DiD designs | | tables-and-plots.md | etable, coefplot, iplot, dtable | Reporting results | | advanced-inference.md | Wild bootstrap, randomization inference, MHT corrections, Gelbach | Advanced statistical inference | | integration.md | Multiple estimation, Poisson, GLM, marginaleffects, online learning | Advanced features | | gotchas.md | Common errors, v0.40 breaking changes, fixest vs pyfixest | Debugging issues |

Reading Order

1. New to pyfixest? Start with quickstart.md then fixed-effects.md
2. Running DiD? Read quickstart.md, then difference-in-differences.md
3. Need IV? Read quickstart.md, then instrumental-variables.md
4. Making tables? Check tables-and-plots.md
5. Coming from R fixest? Read quickstart.md then gotchas.md

Related Skills

| Skill | Relationship | |-------|-------------| | data-scientist | Methodology guidance — load for "why and when" behind methods | | statsmodels | Complement for non-FE models: GLM, time series, diagnostics | | linearmodels | Random effects, GMM, system estimation when pyfixest's FE-only approach is insufficient | | svy | Survey-weighted regression with complex survey designs. pyfixest's clustered SEs account for within-group correlation but do NOT handle full survey design features (stratification, unequal probability weights, FPC). If your data comes from a complex probability survey, use svy for design-based inference | | polars | Data preparation before estimation (convert to pandas before passing to pyfixest) | | plotnine | Custom visualization beyond pyfixest's built-in plots |

Quick Decision Trees

"I need to run a regression"

What kind of regression?
├─ OLS with fixed effects → ./references/quickstart.md
├─ OLS without fixed effects → ./references/quickstart.md
├─ IV / 2SLS → ./references/instrumental-variables.md
├─ Poisson (count data) → ./references/integration.md
├─ Logit / Probit → ./references/integration.md
├─ Quantile regression → ./references/integration.md
└─ Multiple models at once → ./references/integration.md

"I need difference-in-differences"

DiD design?
├─ Simple 2x2 DiD (one treatment date) → ./references/difference-in-differences.md
├─ Staggered treatment timing → ./references/difference-in-differences.md
│   ├─ did2s (Gardner imputation) → ./references/difference-in-differences.md
│   ├─ Local projections DiD → ./references/difference-in-differences.md
│   └─ Sun-Abraham saturated → ./references/difference-in-differences.md
├─ Event study plot → ./references/difference-in-differences.md
├─ Visualize treatment patterns → ./references/difference-in-differences.md
└─ Parallel trends assessment → ./references/difference-in-differences.md

"I need to choose standard errors"

What inference?
├─ Heteroskedasticity-robust (HC1) → ./references/fixed-effects.md
├─ Clustered (one-way / two-way) → ./references/fixed-effects.md
├─ Few clusters (<20) → ./references/advanced-inference.md
│   └─ Wild cluster bootstrap → ./references/advanced-inference.md
├─ HAC / Newey-West → ./references/fixed-effects.md
├─ Randomization inference → ./references/advanced-inference.md
├─ Multiple hypothesis testing → ./references/advanced-inference.md
└─ Causal cluster variance (CCV) → ./references/advanced-inference.md

"I need to present results"

Presenting results?
├─ Regression table (multiple models) → ./references/tables-and-plots.md
├─ Coefficient plot → ./references/tables-and-plots.md
├─ Event study plot → ./references/tables-and-plots.md
├─ Descriptive statistics table → ./references/tables-and-plots.md
└─ LaTeX output → ./references/tables-and-plots.md

"Something isn't working"

Having issues?
├─ Different results from old code → ./references/gotchas.md
├─ feglm with fixed effects error → ./references/gotchas.md
├─ numba installation problems → ./references/gotchas.md
├─ CRV3 memory issues → ./references/gotchas.md
├─ Poisson convergence → ./references/gotchas.md
├─ Formula parsing errors → ./references/gotchas.md
├─ R fixest vs pyfixest differences → ./references/gotchas.md
└─ Singleton warnings → ./references/gotchas.md

File-First Execution in Research Workflows

Important: In data research pipelines (see CLAUDE.md), pyfixest regressions are executed through script files, not interactively. This ensures auditability and reproducibility.

The pattern:

1. Write regression code to scripts/stage8_analysis/{step}_{task-name}.py
2. Execute via Bash with automatic output capture wrapper script
3. Validation results get automatically embedded in scripts as comments
4. If failed, create versioned copy for fixes

Closely read agent_reference/SCRIPT_EXECUTION_REFERENCE.md for the mandatory file-first execution protocol covering complete code file writing, output capture, and file versioning rules. All regression scripts must follow the Inline Audit Trail (IAT) standard — see agent_reference/INLINE_AUDIT_TRAIL.md. For regression code, document model specification choices (why this estimator, why this clustering level, what identifying assumptions) with # INTENT:, # REASONING:, and # ASSUMES: comments.

See:

• agent_reference/WORKFLOW_PHASE4_ANALYSIS.md — Stage 8 (Analysis & Visualization)
• agent_reference/INLINE_AUDIT_TRAIL.md — IAT documentation standard

The examples below show pyfixest syntax. In research workflows, wrap them in scripts following the file-first pattern.

Quick Reference

Essential Import

python
import pyfixest as pf

Core Estimation Functions

| Function | Purpose | |----------|---------| | pf.feols("Y ~ X \| fe", data=df) | OLS with fixed effects | | pf.fepois("Y ~ X \| fe", data=df) | Poisson with fixed effects | | pf.feols("Y ~ X2 \| fe \| X1 ~ Z1", data=df) | IV / 2SLS | | pf.did2s(data, yname, first_stage, second_stage, treatment, cluster) | Gardner (2022) DiD | | pf.event_study(data, yname, idname, tname, gname, estimator) | Unified event study | | pf.lpdid(data, yname, idname, tname, gname) | Local projections DiD |

Formula Syntax Quick Reference

| Pattern | Meaning | Example | |---------|---------|---------| | Y ~ X1 + X2 | No FE | "wage ~ educ + exper" | | Y ~ X \| fe1 + fe2 | With FE | "wage ~ educ \| state + year" | | Y ~ X \| fe \| endog ~ inst | FE + IV | "wage ~ exper \| state \| educ ~ college_prox" | | i(factor, ref=val) | Categorical with ref | "Y ~ i(year, ref=2000) \| state" | | sw(X1, X2) | Stepwise alternatives | "Y ~ sw(educ, exper) \| state" | | csw0(X1, X2) | Cumulative stepwise | "Y ~ csw0(educ, exper) \| state" | | Y1 + Y2 ~ X | Multiple outcomes | "wage + hours ~ educ \| state" |

Post-Estimation Essentials

python
fit = pf.feols("Y ~ X1 + X2 | fe", data=df)

fit.summary()                          # Print results
fit.tidy()                             # DataFrame of coefficients
fit.vcov("hetero")                     # Re-estimate with robust SEs (requires arg)
fit.vcov({"CRV1": "state"})            # Re-estimate with clustered SEs
fit.coef()                             # Coefficient values
fit.se()                               # Standard errors
fit.confint()                          # Confidence intervals
fit.predict()                          # Fitted values
fit.resid()                            # Residuals
fit.fixef()                            # Dict of FE name → numpy array (not a DataFrame)

Reporting

python
pf.etable([fit1, fit2, fit3])          # Regression table
pf.coefplot([fit1, fit2])              # Coefficient plot
pf.iplot(fit)                          # Event study / interaction plot
pf.panelview(data, unit, time, treat)  # Treatment pattern visualization

Topic Index

| Topic | Reference File | |-------|---------------| | Installation | ./references/quickstart.md | | First regression | ./references/quickstart.md | | Formula syntax | ./references/quickstart.md | | SE comparison table | ./references/quickstart.md | | Multi-way fixed effects | ./references/fixed-effects.md | | Standard error types | ./references/fixed-effects.md | | Clustered SEs | ./references/fixed-effects.md | | HAC / Newey-West | ./references/fixed-effects.md | | Backend options | ./references/fixed-effects.md | | IV formula syntax | ./references/instrumental-variables.md | | First-stage diagnostics | ./references/instrumental-variables.md | | Weak instrument tests | ./references/instrumental-variables.md | | TWFE | ./references/difference-in-differences.md | | did2s | ./references/difference-in-differences.md | | Local projections DiD | ./references/difference-in-differences.md | | Sun-Abraham | ./references/difference-in-differences.md | | Event study plots | ./references/difference-in-differences.md | | Parallel trends | ./references/difference-in-differences.md | | panelview | ./references/difference-in-differences.md | | etable | ./references/tables-and-plots.md | | coefplot | ./references/tables-and-plots.md | | iplot | ./references/tables-and-plots.md | | dtable | ./references/tables-and-plots.md | | Wild cluster bootstrap | ./references/advanced-inference.md | | Randomization inference | ./references/advanced-inference.md | | Multiple testing corrections | ./references/advanced-inference.md | | Gelbach decomposition | ./references/advanced-inference.md | | CCV | ./references/advanced-inference.md | | Multiple estimation | ./references/integration.md | | Poisson regression | ./references/integration.md | | GLM (logit/probit) | ./references/integration.md | | Quantile regression | ./references/integration.md | | marginaleffects | ./references/integration.md | | Online learning | ./references/integration.md | | Performance tuning | ./references/integration.md | | Polars DataFrame input | ./references/gotchas.md | | Polars-to-pandas conversion | ./references/quickstart.md | | DiD clustering level | ./references/difference-in-differences.md | | v0.40 breaking changes | ./references/gotchas.md | | feglm FE limitation | ./references/gotchas.md | | numba issues | ./references/gotchas.md | | Formula parsing | ./references/gotchas.md | | R fixest differences | ./references/gotchas.md |

Citation

When this library is used as a primary analytical tool, include in the report's Software & Tools references:

> Berge, L., Butts, K., & McDermott, G. (2026). pyfixest: Fast high-dimensional fixed effects estimation Computer software]. Based on fixest (R).

Cite when: pyfixest is used for regression estimation (OLS, Poisson, IV) or difference-in-differences analysis. Do not cite when: Only imported but no estimation performed.

For method-specific citations (e.g., individual DiD estimators or inference techniques), consult the reference files in this skill and agent_reference/CITATION_REFERENCE.md.