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Panel data econometrics with Python linearmodels; covers pooled OLS, fixed/random effects, Hausman test, clustered SE, Arellano-Bond GMM, and regression tables.

xjtulyc By xjtulyc schedule Updated 3/17/2026
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name: panel-data description: > Panel data econometrics with Python linearmodels; covers pooled OLS, fixed/random effects, Hausman test, clustered SE, Arellano-Bond GMM, and regression tables. tags:

  • economics
  • econometrics
  • panel-data
  • regression
  • python
  • linearmodels version: "1.0.0" authors:
  • name: "awesome-rosetta-skills contributors" github: "@awesome-rosetta-skills" license: "MIT" platforms:
  • claude-code
  • codex
  • gemini-cli
  • cursor dependencies:
  • linearmodels>=5.3
  • pandas>=2.0.0
  • numpy>=1.24.0
  • statsmodels>=0.14.0
  • scipy>=1.10.0
  • tabulate>=0.9.0 last_updated: "2026-03-17"

Panel Data Econometrics with Python linearmodels

This skill covers the full panel data workflow: data setup, estimator selection (Pooled OLS, Fixed Effects, Random Effects), two-way FE, robust standard errors, Arellano-Bond GMM for dynamic panels, and panel unit root tests. All examples use the linearmodels library, which mirrors the Stata/R xtreg/plm API.

1. Setup and Data Structure

pip install linearmodels pandas numpy statsmodels scipy tabulate

import numpy as np
import pandas as pd
import statsmodels.api as sm
from scipy import stats
from linearmodels.panel import (
    PanelOLS,
    PooledOLS,
    RandomEffects,
    BetweenOLS,
    FirstDifferenceOLS,
)
from linearmodels.panel.results import PanelEffectsResults
from statsmodels.stats.stattools import durbin_watson

# ---------------------------------------------------------------------------
# Create a synthetic firm-level panel for illustration
# ---------------------------------------------------------------------------
np.random.seed(42)
n_firms = 200
n_years = 10
firms   = [f"firm_{i:04d}" for i in range(n_firms)]
years   = list(range(2010, 2010 + n_years))

idx     = pd.MultiIndex.from_product([firms, years], names=["firm", "year"])
n       = len(idx)

# Firm fixed effects (unobserved heterogeneity)
fe      = np.repeat(np.random.normal(0, 1, n_firms), n_years)

data = pd.DataFrame({
    "invest":    2 + 0.15 * np.random.normal(0, 1, n) + fe * 0.5
                 + 0.3 * np.random.normal(0, 1, n),
    "rndi":      np.abs(np.random.normal(1, 0.8, n)) + fe * 0.3,
    "size":      np.log(np.abs(np.random.normal(10, 3, n) + fe)),
    "leverage":  np.clip(np.random.beta(2, 5, n) + 0.02 * fe, 0, 1),
    "tobinq":    np.abs(1 + np.random.normal(0, 0.5, n)),
    "cashflow":  np.random.normal(0.1, 0.05, n),
}, index=idx)

# Set the MultiIndex as entity-time for linearmodels
data = data.set_index(pd.MultiIndex.from_tuples(data.index, names=["firm", "year"]))
print(data.head())
print(f"Panel: {n_firms} firms × {n_years} years = {n} observations")

2. Core Estimator Functions

2.1 Fixed Effects (Within) Estimator

def run_fe(
    formula: str,
    df: pd.DataFrame,
    entity: str = "firm",
    time: str = "year",
    entity_effects: bool = True,
    time_effects: bool = False,
    cov_type: str = "clustered",
    cluster_entity: bool = True,
) -> PanelEffectsResults:
    """
    Estimate a panel fixed-effects model.

    Parameters
    ----------
    formula : str
        Patsy-style formula, e.g. 'invest ~ 1 + rndi + size + EntityEffects'.
        Do NOT include EntityEffects/TimeEffects in the formula string;
        pass the flags instead.
    df : pd.DataFrame
        DataFrame with a (entity, time) MultiIndex.
    entity, time : str
        Names of the entity and time dimensions in the MultiIndex.
    entity_effects : bool
        Include entity (within) dummies.
    time_effects : bool
        Include time dummies (two-way FE when combined with entity_effects).
    cov_type : str
        'unadjusted', 'robust', 'clustered', 'kernel'.
    cluster_entity : bool
        When cov_type='clustered', cluster at the entity level.

    Returns
    -------
    PanelEffectsResults
    """
    # Rebuild MultiIndex if needed
    if df.index.names != [entity, time]:
        df = df.copy()
        df.index.names = [entity, time]

    mod = PanelOLS.from_formula(
        formula,
        data=df,
        entity_effects=entity_effects,
        time_effects=time_effects,
    )
    cov_kwargs = {}
    if cov_type == "clustered":
        cov_kwargs = {"cluster_entity": cluster_entity}

    return mod.fit(cov_type=cov_type, **cov_kwargs)


def run_re(
    formula: str,
    df: pd.DataFrame,
    entity: str = "firm",
    time: str = "year",
) -> PanelEffectsResults:
    """
    Estimate a panel random-effects (GLS) model via linearmodels RandomEffects.

    Parameters
    ----------
    formula : str
        Patsy formula without entity/time effect keywords.
    df : pd.DataFrame
        DataFrame with a (entity, time) MultiIndex.

    Returns
    -------
    PanelEffectsResults
    """
    if df.index.names != [entity, time]:
        df = df.copy()
        df.index.names = [entity, time]

    mod = RandomEffects.from_formula(formula, data=df)
    return mod.fit(cov_type="robust")

2.2 Hausman Specification Test

def hausman_test(
    fe_result: PanelEffectsResults,
    re_result: PanelEffectsResults,
) -> dict:
    """
    Perform the Hausman test to choose between FE and RE.

    H0: RE is consistent and efficient (random effects preferred).
    H1: FE is consistent, RE is not (fixed effects preferred).

    Returns
    -------
    dict with keys: stat, df, p_value, decision.
    """
    # Align common coefficients (excluding intercept)
    fe_coef = fe_result.params
    re_coef = re_result.params

    common = fe_coef.index.intersection(re_coef.index)
    common = [c for c in common if c not in ("Intercept", "const")]

    b_fe   = fe_coef[common].values
    b_re   = re_coef[common].values
    diff   = b_fe - b_re

    # Covariance matrices
    V_fe   = fe_result.cov.loc[common, common].values
    V_re   = re_result.cov.loc[common, common].values
    V_diff = V_fe - V_re

    # Make positive definite via eigenvalue clipping
    eigvals, eigvecs = np.linalg.eigh(V_diff)
    eigvals = np.clip(eigvals, 1e-10, None)
    V_diff_pd = eigvecs @ np.diag(eigvals) @ eigvecs.T

    chi2_stat = float(diff @ np.linalg.inv(V_diff_pd) @ diff)
    df_       = len(common)
    p_value   = 1 - stats.chi2.cdf(chi2_stat, df_)

    decision = "Fixed Effects preferred (reject RE)" if p_value < 0.05 else \
               "Random Effects preferred (fail to reject)"

    return {
        "stat":     round(chi2_stat, 4),
        "df":       df_,
        "p_value":  round(p_value, 4),
        "decision": decision,
    }

2.3 Arellano-Bond GMM for Dynamic Panels

def run_arellano_bond(
    df: pd.DataFrame,
    outcome: str,
    regressors: list[str],
    lags: int = 1,
    instrument_lags: tuple = (2, 4),
    entity: str = "firm",
    time: str = "year",
) -> object:
    """
    Estimate a dynamic panel model using Arellano-Bond (AB) first-difference GMM.

    The model is:  y_it = alpha * y_{i,t-1} + X_it * beta + u_it
    Instruments:   levels of y_{i,t-2}, ..., y_{i,t-L} (and X if strictly exogenous).

    This implementation uses statsmodels InstrumentedResiduals as a two-step
    system; for production use the 'linearmodels' IV interface.

    Parameters
    ----------
    df : pd.DataFrame
        Panel DataFrame with MultiIndex (entity, time).
    outcome : str
        Dependent variable column.
    regressors : list[str]
        Exogenous regressors (excluding the lagged DV).
    lags : int
        Number of lagged DV to include.
    instrument_lags : tuple
        (min_lag, max_lag) for internal IV instruments.

    Returns
    -------
    Fitted IVModelResults object.
    """
    from linearmodels.iv import IV2SLS

    if df.index.names != [entity, time]:
        df = df.copy()
        df.index.names = [entity, time]

    # First difference the panel
    fd = df.groupby(level=entity)[[outcome] + regressors].diff().dropna()
    fd.index = df.loc[fd.index].index  # restore MultiIndex

    # Lagged DV in first differences
    lag_name = f"{outcome}_lag{lags}"
    fd[lag_name] = df.groupby(level=entity)[outcome].shift(lags).reindex(fd.index)
    fd = fd.dropna()

    # Use lagged levels as instruments (Arellano-Bond moment conditions)
    inst_cols = []
    for l in range(instrument_lags[0], instrument_lags[1] + 1):
        col = f"{outcome}_inst_lag{l}"
        fd[col] = df.groupby(level=entity)[outcome].shift(l).reindex(fd.index)
        inst_cols.append(col)
    fd = fd.dropna()

    dependent    = fd[outcome]
    exog         = sm.add_constant(fd[regressors])
    endog        = fd[[lag_name]]
    instruments  = fd[inst_cols]

    mod = IV2SLS(dependent, exog, endog, instruments)
    return mod.fit(cov_type="robust")

2.4 Regression Comparison Table

def compare_estimators_table(
    results_dict: dict,
    digits: int = 4,
) -> pd.DataFrame:
    """
    Build a side-by-side regression table from multiple fitted models.

    Parameters
    ----------
    results_dict : dict
        Mapping of model_name -> fitted results object.
    digits : int
        Decimal places.

    Returns
    -------
    pd.DataFrame with coef (se) rows and fit statistics.
    """
    rows = {}
    # Collect all parameter names
    all_params = []
    for res in results_dict.values():
        all_params.extend(res.params.index.tolist())
    all_params = list(dict.fromkeys(all_params))  # deduplicate, preserve order

    for param in all_params:
        row = {}
        for name, res in results_dict.items():
            if param in res.params:
                coef = res.params[param]
                se   = res.std_errors[param]
                pval = res.pvalues[param]
                stars = "***" if pval < 0.01 else "**" if pval < 0.05 else "*" if pval < 0.1 else ""
                row[name] = f"{coef:.{digits}f}{stars} ({se:.{digits}f})"
            else:
                row[name] = ""
        rows[param] = row

    table = pd.DataFrame(rows).T

    # Append fit stats
    stats_rows = {}
    for name, res in results_dict.items():
        r2   = getattr(res, "rsquared", getattr(res, "rsquared_between", np.nan))
        nobs = int(getattr(res, "nobs", np.nan))
        stats_rows[name] = {"R²": f"{r2:.4f}", "N": str(nobs)}

    stats_df = pd.DataFrame(stats_rows).T.rename_axis("Model")
    print("\n=== Regression Table ===")
    print(table.to_string())
    print("\n=== Fit Statistics ===")
    print(stats_df.to_string())
    return table

3. Panel Unit Root Tests

def panel_unit_root_tests(df: pd.DataFrame, col: str, entity: str = "firm") -> None:
    """
    Run LLC and IPS panel unit root tests via statsmodels.
    Prints test statistics and p-values for each entity.
    """
    from statsmodels.tsa.stattools import adfuller

    series_list = []
    for ent, grp in df.groupby(level=entity):
        s = grp[col].dropna()
        if len(s) > 10:
            series_list.append(s.values)

    adf_stats = []
    for s in series_list:
        try:
            result = adfuller(s, autolag="AIC")
            adf_stats.append(result[0])
        except Exception:
            pass

    # IPS test: average of individual ADF t-statistics
    if adf_stats:
        t_bar = np.mean(adf_stats)
        n     = len(adf_stats)
        # Approximate critical value table from Im, Pesaran, Shin (2003)
        print(f"IPS t-bar statistic: {t_bar:.4f} (N={n})")
        print("  Rule of thumb: t-bar < -1.73 → reject H0 of unit root at 5%")
    else:
        print("Insufficient data for unit root tests.")

4. Testing Serial Correlation (Wooldridge Test)

def wooldridge_serial_corr(
    formula: str,
    df: pd.DataFrame,
    entity: str = "firm",
    time: str = "year",
) -> dict:
    """
    Wooldridge (2002) test for serial correlation in panel FE residuals.
    Regress FD residuals on lagged FD residuals; test coefficient = -0.5.
    """
    if df.index.names != [entity, time]:
        df = df.copy()
        df.index.names = [entity, time]

    res = run_fe(formula, df, entity, time, cov_type="clustered")
    resid = pd.Series(res.resids, index=df.index, name="resid")

    fd_resid = resid.groupby(level=entity).diff().dropna()
    fd_lag   = resid.groupby(level=entity).shift(1).reindex(fd_resid.index).dropna()
    common   = fd_resid.index.intersection(fd_lag.index)
    fd_resid, fd_lag = fd_resid.loc[common], fd_lag.loc[common]

    X = sm.add_constant(fd_lag.values.reshape(-1, 1))
    ols = sm.OLS(fd_resid.values, X).fit()
    coef, se = ols.params[1], ols.bse[1]
    t_stat  = (coef - (-0.5)) / se
    p_value = 2 * (1 - stats.t.cdf(abs(t_stat), df=ols.df_resid))

    print(f"Wooldridge test: coef on lagged FD resid = {coef:.4f}, "
          f"t({ols.df_resid:.0f}) = {t_stat:.4f}, p = {p_value:.4f}")
    print("H0: no first-order serial correlation in idiosyncratic errors.")
    return {"coef": coef, "t_stat": t_stat, "p_value": p_value}

5. Example A — Firm R&D Panel: FE vs RE with Hausman Decision Rule

import numpy as np
import pandas as pd
from linearmodels.panel import PanelOLS, RandomEffects

# ---- Use the synthetic data created in Section 1 --------------------------------
formula_fe = "invest ~ 1 + rndi + size + leverage + tobinq + EntityEffects"
formula_re = "invest ~ 1 + rndi + size + leverage + tobinq"

# Pooled OLS (baseline, ignores heterogeneity)
pooled_mod = PooledOLS.from_formula("invest ~ 1 + rndi + size + leverage + tobinq", data=data)
pooled_res = pooled_mod.fit(cov_type="robust")

# Fixed effects
fe_res = run_fe(
    "invest ~ 1 + rndi + size + leverage + tobinq",
    data,
    entity_effects=True,
    time_effects=False,
    cov_type="clustered",
    cluster_entity=True,
)

# Two-way fixed effects
twfe_res = run_fe(
    "invest ~ 1 + rndi + size + leverage + tobinq",
    data,
    entity_effects=True,
    time_effects=True,
    cov_type="clustered",
    cluster_entity=True,
)

# Random effects
re_res = run_re("invest ~ 1 + rndi + size + leverage + tobinq", data)

# Hausman test
hausman = hausman_test(fe_res, re_res)
print("\n=== Hausman Specification Test ===")
for k, v in hausman.items():
    print(f"  {k}: {v}")

# Comparison table
compare_estimators_table({
    "Pooled OLS": pooled_res,
    "FE (Entity)": fe_res,
    "Two-Way FE":  twfe_res,
    "RE":          re_res,
})

# R² components (FE)
print(f"\nFE Within R²:   {fe_res.rsquared:.4f}")
print(f"FE Between R²:  {fe_res.rsquared_between:.4f}")
print(f"FE Overall R²:  {fe_res.rsquared_overall:.4f}")

6. Example B — Arellano-Bond GMM for Dynamic Investment Model

import numpy as np
import pandas as pd

# ---- Add a lagged investment variable to simulate dynamic panel ------------------
np.random.seed(7)
data_dyn = data.copy()

# Generate a correlated lagged investment (AR(1) component)
data_dyn = data_dyn.sort_index()
invest_lag = data_dyn.groupby(level="firm")["invest"].shift(1)
data_dyn["invest_lag1"] = invest_lag

# Drop NaN introduced by lagging
data_dyn = data_dyn.dropna(subset=["invest_lag1"])

print(f"Dynamic panel: {data_dyn.shape[0]} obs after removing lag-1 NaN")

# ---- Naive FE (biased in dynamic panel — Nickell bias) --------------------------
fe_biased = run_fe(
    "invest ~ 1 + invest_lag1 + rndi + size + leverage",
    data_dyn,
    entity_effects=True,
    time_effects=False,
    cov_type="clustered",
)
print("\n=== Naive FE (Nickell-biased) ===")
print(fe_biased.summary.tables[1])

# ---- Arellano-Bond GMM ----------------------------------------------------------
ab_res = run_arellano_bond(
    df=data_dyn,
    outcome="invest",
    regressors=["rndi", "size", "leverage"],
    lags=1,
    instrument_lags=(2, 4),
)
print("\n=== Arellano-Bond GMM ===")
print(ab_res.summary.tables[1])

# ---- Compare persistence estimates ----------------------------------------------
print("\n=== Persistence (lagged DV coefficient) ===")
print(f"  FE (biased):   {fe_biased.params.get('invest_lag1', float('nan')):.4f}")
print(f"  AB GMM:        {ab_res.params.get('invest_lag1', float('nan')):.4f}")
print("  (AB typically corrects downward Nickell bias in FE)")

# ---- Serial correlation test on FE residuals ------------------------------------
wtest = wooldridge_serial_corr(
    "invest ~ 1 + rndi + size + leverage + tobinq",
    data,
)

7. Driscoll-Kraay Standard Errors

def driscoll_kraay_se(
    formula: str,
    df: pd.DataFrame,
    bandwidth: int = 3,
    entity: str = "firm",
    time: str = "year",
) -> PanelEffectsResults:
    """
    Estimate FE model with Driscoll-Kraay (1998) standard errors, which are
    robust to cross-sectional dependence and temporal autocorrelation.
    linearmodels implements these via cov_type='kernel'.
    """
    if df.index.names != [entity, time]:
        df = df.copy()
        df.index.names = [entity, time]

    mod = PanelOLS.from_formula(formula, data=df, entity_effects=True)
    return mod.fit(cov_type="kernel", bandwidth=bandwidth)


# Usage
dk_res = driscoll_kraay_se(
    "invest ~ 1 + rndi + size + leverage + tobinq",
    data,
    bandwidth=3,
)
print("\n=== Driscoll-Kraay SE ===")
print(dk_res.summary.tables[1])

8. Tips and Common Pitfalls

  • MultiIndex requirement: linearmodels requires a strict (entity, time) MultiIndex. Always call df.index.names = ['entity', 'time'] before fitting.
  • EntityEffects in formula vs flag: Do NOT put EntityEffects in the formula string when using PanelOLS(entity_effects=True). They are mutually exclusive.
  • Hausman test with clustered SE: The standard Hausman test assumes homoskedastic errors. With clustered SE, use the Mundlak (1978) regression test instead: add group-means of time-varying regressors to the RE model and test joint significance.
  • Arellano-Bond validity: Check the Sargan/Hansen J-test for instrument validity and AR(2) test. Presence of AR(2) violates the moment conditions.
  • Nickell bias: In short panels (small T, large N) with lagged DV, FE estimates are biased of order O(1/T). AB GMM corrects this via first-differencing + IV.
  • Unbalanced panels: linearmodels handles unbalanced panels natively. Rows with missing outcomes are dropped automatically.
Install via CLI
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill panel-data
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