statsmodels – Data & Science skill for AI agents

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](references/review.md) 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](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](references/modeling_capabilities.md): linear

models, GLMs, discrete choice, time series, and the statistical tests and diagnostics.

• [references/model_selection.md](references/model_selection.md): the R-style formula API

and model comparison.

• Per-topic detail: [references/linear_models.md](references/linear_models.md),

[references/glm.md](references/glm.md),

[references/discrete_choice.md](references/discrete_choice.md),

[references/time_series.md](references/time_series.md), and

[references/stats_diagnostics.md](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

2. Influence diagnostics: Identify and investigate influential observations

3. Out-of-sample validation: Test on holdout set or cross-validate

4. Compare models: Use AIC/BIC only for comparable likelihoods on the same response and rows; regular nested-model LR tests need interior parameters and valid likelihood assumptions

Reporting

1. Comprehensive summary: Use .summary() for detailed output

2. Document decisions: Note transformations, excluded observations

3. Interpret carefully: Account for link functions (e.g., exp(β) for log link)

4. Visualize: Plot predictions, confidence intervals, diagnostics

Common Workflows

Workflow 1: Linear Regression Analysis

1. Explore data (plots, descriptives)

2. Fit initial OLS model

3. Check residual diagnostics

4. Test for heteroskedasticity, autocorrelation

5. Check for multicollinearity (VIF)

6. Identify influential observations

7. Refit with robust SEs if needed

8. Interpret coefficients and inference

9. Validate on holdout or via CV

Workflow 2: Binary Classification

1. Fit logistic regression (Logit)

2. Check for convergence issues

3. Interpret odds ratios

4. Calculate marginal effects

5. Evaluate classification performance (AUC, confusion matrix)

6. Check for influential observations

7. Compare with alternative models (Probit)

8. Validate predictions on test set

Workflow 3: Count Data Analysis

1. Fit Poisson regression

2. Check for overdispersion

3. If overdispersed, fit Negative Binomial

4. Check for excess zeros (consider ZIP/ZINB)

5. Interpret rate ratios

6. Assess goodness of fit

7. Compare models via AIC

8. Validate predictions

Workflow 4: Time Series Forecasting

1. Plot series, check for trend/seasonality

2. Test for stationarity (ADF, KPSS)

3. Choose deterministic terms and differencing using domain context, plots and tests; do not treat failure to reject a unit root as proof

4. Use ACF/PACF for candidate orders, then compare converged fits on training data

5. Fit ARIMA or SARIMAX

6. Check residual diagnostics (Ljung-Box)

7. Generate forecasts with model-based prediction intervals and required future exogenous inputs

8. Evaluate forecast accuracy on test set

Reference Documentation

This skill includes comprehensive reference files for detailed guidance:

references/linear_models.md

Detailed coverage of linear regression models including:

• OLS, WLS, GLS, GLSAR, Quantile Regression
• Mixed effects models
• Recursive and rolling regression
• Comprehensive diagnostics (heteroskedasticity, autocorrelation, multicollinearity)
• Influence statistics and outlier detection
• Robust standard errors (HC, HAC, cluster)
• Hypothesis testing and model comparison

references/glm.md

Complete guide to generalized linear models:

• All distribution families (Binomial, Poisson, Gamma, etc.)
• Link functions and when to use each
• Model fitting and interpretation
• Pseudo R-squared and goodness of fit
• Diagnostics and residual analysis
• Applications (logistic, Poisson, Gamma regression)

references/discrete_choice.md

Comprehensive guide to discrete outcome models:

• Binary models (Logit, Probit)
• Multinomial models (MNLogit, Conditional Logit)
• Count models (Poisson, Negative Binomial, Zero-Inflated, Hurdle)
• Ordinal models
• Marginal effects and interpretation
• Model diagnostics and comparison

references/time_series.md

In-depth time series analysis guidance:

• Univariate models (AR, ARIMA, SARIMAX, Exponential Smoothing)
• Multivariate models (VAR, VARMAX, Dynamic Factor)
• State space models
• Stationarity testing and diagnostics
• Forecasting methods and evaluation
• Granger causality, IRF, FEVD

references/stats_diagnostics.md

Comprehensive statistical testing and diagnostics:

• Residual diagnostics (autocorrelation, heteroskedasticity, normality)
• Influence and outlier detection
• Hypothesis tests (parametric and non-parametric)
• ANOVA and post-hoc tests
• Multiple comparisons correction
• Robust covariance matrices
• Power analysis and effect sizes

When to reference:

• Need detailed parameter explanations
• Choosing between similar models
• Troubleshooting convergence or diagnostic issues
• Understanding specific test statistics
• Looking for code examples for advanced features

Search patterns:

# Find information about specific models
rg "Quantile Regression" references/

# Find diagnostic tests
rg "Breusch-Pagan" references/stats_diagnostics.md

# Find time series guidance
rg "SARIMAX" references/time_se

… (full content is in the download package)