SKILL.md
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.