Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
license
BSD-3-Clause license
metadata
{"skill-author":"K-Dense Inc."}
verified
false
lastVerifiedAt
"2026-02-19T05:29:09.098Z"
source
builtin
trust_score
100
provenance_sha
a5e966b54094eb5f
Statsmodels: Statistical Modeling and Econometrics
Overview
Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods. Apply this skill for rigorous statistical analysis, from simple linear regression to complex time series models and econometric analyses.
# Compare models using AIC/BIC
models = {
'Model 1': model1_results,
'Model 2': model2_results,
'Model 3': model3_results
}
comparison = pd.DataFrame({
'AIC': {name: res.aic for name, res in models.items()},
'BIC': {name: res.bic for name, res in models.items()},
'Log-Likelihood': {name: res.llf for name, res in models.items()}
})
print(comparison.sort_values('AIC'))
# Lower AIC/BIC indicates better model
Likelihood Ratio Test (Nested Models)
# For nested models (one is subset of the other)from scipy import stats
lr_stat = 2 * (full_model.llf - reduced_model.llf)
df = full_model.df_model - reduced_model.df_model
p_value = 1 - stats.chi2.cdf(lr_stat, df)
print(f"LR statistic: {lr_stat:.4f}")
print(f"p-value: {p_value:.4f}")
if p_value < 0.05:
print("Full model significantly better")
else:
print("Reduced model preferred (parsimony)")
Cross-Validation
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error
kf = KFold(n_splits=5, shuffle=True, random_state=42)
cv_scores = []
for train_idx, val_idx in kf.split(X):
X_train, X_val = X.iloc[train_idx], X.iloc[val_idx]
y_train, y_val = y.iloc[train_idx], y.iloc[val_idx]
# Fit model
model = sm.OLS(y_train, X_train).fit()
# Predict
y_pred = model.predict(X_val)
# Score
rmse = np.sqrt(mean_squared_error(y_val, y_pred))
cv_scores.append(rmse)
print(f"CV RMSE: {np.mean(cv_scores):.4f} ± {np.std(cv_scores):.4f}")
Best Practices
Data Preparation
Always add constant: Use sm.add_constant() unless excluding intercept
Check for missing values: Handle or impute before fitting
Scale if needed: Improves convergence, interpretation (but not required for tree models)
Encode categoricals: Use formula API or manual dummy coding
Model Building
Start simple: Begin with basic model, add complexity as needed
Check assumptions: Test residuals, heteroskedasticity, autocorrelation
Use appropriate model: Match model to outcome type (binary→Logit, count→Poisson)
Consider alternatives: If assumptions violated, use robust methods or different model
Inference
Report effect sizes: Not just p-values
Use robust SEs: When heteroskedasticity or clustering present
Multiple comparisons: Correct when testing many hypotheses
Confidence intervals: Always report alongside point estimates
Model Evaluation
Check residuals: Plot residuals vs fitted, Q-Q plot
Influence diagnostics: Identify and investigate influential observations
Out-of-sample validation: Test on holdout set or cross-validate
Compare models: Use AIC/BIC for non-nested, LR test for nested
Reporting
Comprehensive summary: Use .summary() for detailed output
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