Statsmodels: Statistical Modeling and Econometrics

Overview

Statsmodels provides estimation, inference and diagnostics for regression, time series and econometric models. A successful fit establishes numerical execution; causal identification, calibrated uncertainty and model adequacy require a defensible study design and assumptions.

Current Compatibility

Reviewed against statsmodels 0.15.0 (released August 27, 2026). Native checks used Python 3.13, NumPy 2.5.3, SciPy 1.18.1, pandas 3.0.6, matplotlib 3.11.2 and scikit-learn 1.9.1. Install in a dedicated environment:

uv pip install statsmodels==0.15.0 numpy==2.5.3 scipy==1.18.1 pandas==3.0.6 matplotlib==3.11.2 scikit-learn==1.9.1

Use statsmodels.api and statsmodels.formula.api for stable high-level imports, and direct module imports when examples require newer or specialized classes such as HurdleCountModel.

The review and source ledger records API coverage and verification limits. The quick start is executable; topic references are contextual fragments requiring the named data and a matching model result. In 0.15, use result_object=True and named fields for ADF/KPSS and other transitioning tests; prefer rng= where statsmodels formerly accepted seed or random_state.

When to Use This Skill

This skill should be used when:

• Fitting regression models (OLS, WLS, GLS, quantile regression)
• Performing generalized linear modeling (logistic, Poisson, Gamma, etc.)
• Analyzing discrete outcomes (binary, multinomial, count, ordinal)
• Conducting time series analysis (ARIMA, SARIMAX, VAR, forecasting)
• Running statistical tests and diagnostics
• Testing model assumptions (heteroskedasticity, autocorrelation, normality)
• Detecting outliers and influential observations
• Comparing models (AIC/BIC, likelihood ratio tests)
• Estimating causal effects
• Producing publication-ready statistical tables and inference

Quick Start, Capabilities, and Model Selection

• references/quick_start_guide.md: minimal worked examples for OLS, logistic regression, ARIMA, and GLM, and how to read the summary.
• references/modeling_capabilities.md: linear models, GLMs, discrete choice, time series, and the statistical tests and diagnostics.
• references/model_selection.md: the R-style formula API and model comparison.
• Per-topic detail: references/linear_models.md, references/glm.md, references/discrete_choice.md, references/time_series.md, and references/stats_diagnostics.md.

statsmodels supports inference and prediction, including forecasting. Match validation to the sampling design: grouped splits for repeated units, chronological splits for time series, and preprocessing learned on training data only.

Best Practices

Data Preparation

1. Specify the intercept: Array OLS/GLM/Logit need an explicit constant; formula models include one by default. OrderedModel and ConditionalLogit must not receive a constant.
2. Check for missing values: For array-based models, use missing="raise" during construction to catch unexpected NaNs; the default missing="none" does not check and can yield all-NaN estimates. If dropping rows is justified, record retained row IDs and compare models on the same observations. Fit any imputation on training data only.
3. Scale if needed: Can improve conditioning and convergence; record units and estimate scaling on training data
4. Encode categoricals: Use formula API or manual dummy coding

Model Building

1. Start simple: Begin with basic model, add complexity as needed
2. Check assumptions: Test residuals, heteroskedasticity, autocorrelation
3. Use appropriate model: Match model to outcome type (binary→Logit, count→Poisson)
4. Consider alternatives: If assumptions violated, use robust methods or different model

Inference

1. Report effect sizes: Not just p-values
2. Use robust SEs: When heteroskedasticity or clustering present
3. Multiple comparisons: Correct when testing many hypotheses
4. Confidence intervals: Always report alongside point estimates

Model Evaluation

1. Check residuals: Plot residuals vs fitted, Q-Q plot

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