/pyfixest

name: pyfixest
description: >-
  Fast high-dimensional fixed effects: OLS, Poisson, IV with multi-way FE; DiD (TWFE, did2s, Sun-Abraham); clustered SEs; etable/coefplot/iplot. Use for FE regressions or DiD. For panel RE/between use linearmodels; for GLM without FE use statsmodels.
metadata:
  audience: research-coders
  domain: python-library
  library-version: "0.40.0"
  skill-last-updated: "2026-03-27"

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
│