Statsmodels: Statistical Modeling and Econometrics
Detailed Guide
Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.
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
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
1---2name: statsmodels3description: Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods.4license: BSD-3-Clause license5---67# Statsmodels: Statistical Modeling and Econometrics89## Detailed Guide1011Read [the detailed guide](references/detailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.1213## When to Use This Skill1415This skill should be used when:16- Fitting regression models (OLS, WLS, GLS, quantile regression)17- Performing generalized linear modeling (logistic, Poisson, Gamma, etc.)18- Analyzing discrete outcomes (binary, multinomial, count, ordinal)19- Conducting time series analysis (ARIMA, SARIMAX, VAR, forecasting)20- Running statistical tests and diagnostics21- Testing model assumptions (heteroskedasticity, autocorrelation, normality)22- Detecting outliers and influential observations23- Comparing models (AIC/BIC, likelihood ratio tests)24- Estimating causal effects25- Producing publication-ready statistical tables and inference2627## Limitations28- Use this skill only when the task clearly matches the scope described above.29- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.30- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
FrancoStino