Write and debug Python regressions with PyFixest using a reference for model formulas, estimation functions, and standard errors. It also covers extracting results, comparing models, and producing regression tables and plots, including models that account for group differences.
Dense, machine-readable API reference for PyFixest — high-dimensional fixed-effects OLS/WLS/IV and Poisson (feols, fepois, feglm), clustered/robust standard errors, R-style formula syntax, and post-estimation. Use when writing or debugging Python fixed-effects regressions with the pyfixest package.
Review the instructions the agent will receive before you install.
Dense, machine-readable reference for LLMs. No prose padding. Version: matches latest PyFixest release.
import pyfixest as pf
pf.feols(
fml: str, # Formula: "Y ~ X1 + X2 | fe1 + fe2" or IV: "Y ~ exog | fe | endog ~ inst"
data: pd.DataFrame,
vcov: str | dict = None, # "iid", "HC1"-"HC3", {"CRV1": "clust"}, {"CRV3": "clust"}, {"CRV1": "c1+c2"}
weights: str = None, # Column name for weights
ssc: dict = None, # Small sample correction, see pf.ssc()
fixef_rm: str = "singleton", # "none" or "singleton"
drop_intercept: bool = False,
split: str = None, # Column name to split sample by
fsplit: str = None, # Like split but also fits on full sample
weights_type: str = "aweights", # "aweights" or "fweights"
solver: str = "scipy.linalg.solve",
lean: bool = False,
) -> Feols | FixestMulti
Returns Feols for single model, FixestMulti for multiple estimation syntax.
pf.fepois(
fml: str, # "Y ~ X1 + X2 | fe1 + fe2"
data: pd.DataFrame,
vcov: str | dict = None,
ssc: dict = None,
fixef_rm: str = "singleton",
iwls_tol: float = 1e-08,
iwls_maxiter: int = 25,
separation_check: list[str] = None, # ["fe"] to check for separated FE
split: str = None,
fsplit: str = None,
) -> Fepois | FixestMulti
pf.feglm(
fml: str,
data: pd.DataFrame,
family: str, # "gaussian", "logit", "probit"
vcov: str | dict = None,
separation_check: list[str] = None,
split: str = None,
fsplit: str = None,
) -> Feglm | FixestMulti
pf.quantreg(
fml: str,
data: pd.DataFrame,
vcov: str | dict = "nid",
quantile: float | list[float] = 0.5, # Single or list of quantiles
method: str = "fn", # "fn" (Frisch-Newton)
split: str = None,
fsplit: str = None,
) -> Feols | FixestMulti
"Y ~ X1 + X2" # OLS
"Y ~ X1 + X2 | fe1" # OLS + one FE
"Y ~ X1 + X2 | fe1 + fe2" # OLS + two-way FE
"Y ~ X1 + C(categorical)" # Categorical variable as dummies
"Y ~ X1 + i(factor, ref=0)" # Factor variable for event studies
"Y ~ X1 + i(f1, X2)" # Interaction: factor × continuous
"Y ~ 1 | fe1 | X1 ~ Z1" # IV: depvar ~ exog | fe | endog ~ inst
"Y ~ 1 | X1 ~ Z1 + Z2" # IV without FE
"Y ~ X1:X2" # Interaction only
"Y ~ X1*X2" # X1 + X2 + X1:X2
"Y ~ X1 + I(X1**2)" # Polynomial term
"Y ~ X1 + sw(X2, X3)" # Stepwise: two models, X2 then X3
"Y ~ X1 + sw0(X2, X3)" # Stepwise with empty: three models
"Y ~ X1 + csw(X2, X3)" # Cumulative stepwise: X2, then X2+X3
"Y ~ X1 + csw0(X2, X3)" # Cumulative with empty: three models
"Y + Y2 ~ X1" # Multiple dependent variables
"Y ~ X1 | csw0(fe1, fe2)" # Stepwise fixed effects
pf.feols("Y ~ X1 | fe1", data=data, split="group_var") # Separate by group
pf.feols("Y ~ X1 | fe1", data=data, fsplit="group_var") # Separate + full sample
fit.summary() # Print summary
fit.tidy(alpha=0.05) # pd.DataFrame: Estimate, Std. Error, t value, Pr(>|t|), CI
fit.coef() # pd.Series of coefficients
fit.se() # pd.Series of standard errors
fit.tstat() # pd.Series of t-statistics
fit.pvalue() # pd.Series of p-values
fit.confint(alpha=0.05) # pd.DataFrame of confidence intervals
fit.confint(joint=True) # Simultaneous confidence bands (multiplier bootstrap)
fit.vcov("iid") # IID standard errors
fit.vcov("HC1") # Heteroskedasticity-robust
fit.vcov({"CRV1": "cluster_var"}) # One-way cluster-robust
fit.vcov({"CRV3": "cluster_var"}) # CRV3 cluster-robust
fit.vcov({"CRV1": "c1 + c2"}) # Two-way clustering
Returns self — chainable: fit.vcov("HC1").summary().
fit.coefplot() # Coefficient plot
pf.coefplot([fit1, fit2], keep="X1") # Compare models
pf.iplot([fit1, fit2], coord_flip=False) # Event study plot (for i() vars)
pf.qplot(fit_qr) # Quantile regression plot
fit.predict() # In-sample predictions
fit.predict(newdata=df_new) # Out-of-sample
fit.predict(type="response") # Response scale (GLMs)
fit.predict(type="link") # Link scale (GLMs)
# Wild cluster bootstrap
fit.wildboottest(param="X1", reps=999, cluster="clust_var")
# Randomization inference
fit.ritest(resampvar="X1=0", reps=1000, cluster="group_id")
# Causal cluster variance estimator (Abadie et al. 2023)
fit.ccv(treatment="treat_var", pk=0.05, n_splits=2, seed=42)
# Wald test: H0: beta = 0
fit.wald_test(R=np.eye(k))
# Wald test: H0: R @ beta = q
fit.wald_test(R=R_matrix, q=q_vector)
fit_iv._model_1st_stage # First stage Feols object
fit_iv._f_stat_1st_stage # First stage F-statistic
fit_iv.IV_Diag() # Run IV diagnostics
fit_iv._eff_F # Effective F-stat (Olea & Pflueger 2013)
fit.update(X_new, y_new) # Sherman-Morrison coefficient update
pf.etable(
models, # Feols, list[Feols], or FixestMulti
type: str = "gt", # "gt" (Great Tables), "tex" (LaTeX), "md" (markdown), "df" (DataFrame)
signif_code: list = None, # e.g. [0.001, 0.01, 0.05]
coef_fmt: str = "b \n (se)", # Format: b=coef, se=SE, p=pval, t=tstat, ci_l, ci_u
keep: str | list = None, # Regex pattern(s) to keep
drop: str | list = None, # Regex pattern(s) to drop
labels: dict = None, # {"old_name": "New Label"}
felabels: dict = None, # {"fe_var": "FE Label"}
show_fe: bool = True,
show_se_type: bool = True,
notes: str = "",
model_heads: list = None, # Custom column headers
caption: str = None, # Via kwargs
file_name: str = None, # Save to file (.tex, .html)
)
pf.summary(models, digits=3) # models: Feols, list, or FixestMulti
pf.dtable(
df: pd.DataFrame,
vars: list, # Column names
stats: list = None, # ["count", "mean", "std", "min", "max", "median"]
bycol: list[str] = None, # Group columns (shown as separate column groups)
byrow: str = None, # Group variable (shown as row sections)
type: str = "gt", # "gt", "tex", "md", "df"
labels: dict = None,
digits: int = 2,
)
pf.bonferroni(models, param="X1") # Bonferroni adjusted p-values
pf.rwolf(models, param="X1", reps=9999, seed=42) # Romano-Wolf
pf.wyoung(models, param="X1", reps=9999, seed=42) # Westfall-Young
pf.event_study(
data: pd.DataFrame,
yname: str, # Outcome column
idname: str, # Unit ID column
tname: str, # Time column
gname: str, # Group (first treatment period) column
xfml: str = None, # Additional covariates formula
cluster: str = None, # Cluster variable
estimator: str = "twfe", # "twfe" or "did2s"
att: bool = True,
)
pf.did2s(
data: pd.DataFrame,
yname: str,
first_stage: str, # "~ covariates | fe1 + fe2"
second_stage: str, # "~ i(rel_year, ref=-1.0)"
treatment: str, # Treatment indicator column
cluster: str,
weights: str = None,
)
pf.lpdid(
data: pd.DataFrame,
yname: str,
idname: str,
tname: str,
gname: str,
vcov: str | dict = None,
pre_window: int = None,
post_window: int = None,
never_treated: int = 0,
att: bool = True,
xfml: str = None,
)
pf.panelview(data, unit="unit_col", time="time_col", treat="treat_col")
pf.ssc(
k_adj: bool = True, # Adjust for number of estimated parameters
k_fixef: str = "nonnested", # "nonnested" or "nested" FE adjustment
G_adj: bool = True, # Adjust for number of clusters
G_df: str = "min", # "min" or "conventional"
)
# Usage:
pf.feols("Y ~ X1 | fe1", data=data, ssc=pf.ssc(k_adj=True))
pf.get_data(N=1000, seed=1234, model="Feols") # Synthetic data: "Feols" or "Fepois"
pf.get_twin_data(N_pairs=500, seed=42) # Twin study data (returns to education)
pf.get_worker_panel(N_workers=500, N_firms=50, N_years=11, seed=42) # Worker-firm panel
| vcov | Description |
|---|---|
"iid" | Spherical errors (homoskedastic, uncorrelated) |
"HC1" | Heteroskedasticity-robust (White) |
"HC2" | HC2 robust |
"HC3" | HC3 robust (jackknife-like) |
{"CRV1": "var"} | One-way cluster-robust |
{"CRV3": "var"} | CRV3 cluster-robust |
{"CRV1": "v1 + v2"} | Two-way clustering |
Default: CRV1 clustered by first FE variable (if FE present), else "iid".
# Basic OLS with FE and clustering
fit = pf.feols("Y ~ X1 + X2 | fe1 + fe2", data=df, vcov={"CRV1": "fe1"})
# IV regression
fit_iv = pf.feols("Y ~ exog | fe1 | endog ~ instrument", data=df)
# Multiple specifications at once
fits = pf.feols("Y ~ X1 | csw0(fe1, fe2, fe3)", data=df)
fits.etable()
# Poisson with FE
fit_pois = pf.fepois("count ~ X1 + X2 | fe1", data=df)
# Event study
fit_es = pf.feols("Y ~ i(rel_time, ref=-1) | unit + time", data=df)
pf.iplot(fit_es)
# Publication table
pf.etable([fit1, fit2, fit3], type="tex", file_name="table1.tex",
labels={"X1": "Treatment"}, felabels={"fe1": "Unit FE"})
# Adjust SE after estimation
fit.vcov({"CRV1": "cluster"}).summary()
# Compare R fixest syntax → PyFixest
# R: feols(Y ~ X1 | fe1, data, cluster = ~fe1)
# Py: pf.feols("Y ~ X1 | fe1", data=data, vcov={"CRV1": "fe1"})
When multiple estimation syntax is used, returns FixestMulti:
multi = pf.feols("Y + Y2 ~ X1 | csw0(fe1, fe2)", data=df)
multi.etable() # Table of all models
multi.summary() # Print all summaries
multi.coefplot() # Plot all models
multi.vcov("HC1") # Update all models' inference
multi.fetch_model(0) # Get first Feols object
multi.all_fitted_models["Y~X1"] # Access by formula key
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Turn a broad research idea into clear, testable questions. Compare familiar and less conventional approaches, structure the selected question around its population and methods, and assess variables, feasibility, and potential theoretical contributions.
Helps build a research framework by comparing theories, mapping relationships between variables, and deriving testable hypotheses. Explores less conventional options, examines weaknesses and competing explanations, and produces diagrams with anticipated reviewer objections.
Clarify the philosophical assumptions behind a research project and check whether they fit the proposed methods. Get a reasoned research stance, draft proposal wording, and guidance for explaining how the researcher's background and values shape the study.
Plan and draft academic papers through specialized agent roles covering literature, arguments, citations, and review. Get chapter guidance, revision roadmaps, Traditional Chinese and English abstracts, and documents formatted as LaTeX, DOCX, PDF, or Markdown.
Turn a detailed outline into a philosophy or interdisciplinary manuscript. Draft chapters with repeated quality reviews, check terminology and arguments across sections, and prepare the manuscript, evaluation reports, abstract, and metadata for submission.
Simulate academic peer review through five reviewer roles tailored to the paper's field. Receive separate critiques, an editorial decision, and a prioritized revision roadmap, or check whether a revised manuscript addresses earlier comments.