Fit OLS, GLM, and time-series models with diagnostics in Python.
npx skills add https://github.com/rubensliv/k-dense-ai --skill statsmodels-rubensliv
Please help me install this Agent Skill. Skill: statsmodels Source: https://github.com/rubensliv/k-dense-ai/tree/main/scientific-skills/statsmodels Command: npx skills add https://github.com/rubensliv/k-dense-ai --skill statsmodels-rubensliv
Statsmodels provides a comprehensive toolkit for building, diagnosing, and interpreting statistical models in Python, enabling rigorous inference and econometric analysis.
Fit a basic OLS model to your data and display the full summary.
High-intent search queries and answers about installing and using this skill.
You can run OLS regression with robust standard errors using statistical modeling tools like statsmodels, which supports linear modeling and diagnostic tests for econometric inference. It provides comprehensive results summaries including robust standard errors and hypothesis testing outputs.
Fitting ARIMA and SARIMAX time-series models for econometric analysis requires a statistical modeling framework that supports state-space models and comprehensive diagnostics. These frameworks enable rigorous time-series forecasting and policy evaluation directly within Python.
Yes, you can use an R-style formula API for generalized linear modeling with categorical variables using statistical modeling frameworks like statsmodels. This approach allows easy handling of categorical data, marginal effects calculation, and fitting GLM models with various families and links.
Yes, statistical modeling frameworks like statsmodels support mixed-effects models and VAR for time-series analysis and econometric inference. It enables fitting MixedLM, VAR, and state-space models with comprehensive diagnostic tests and model comparison using information criteria like AIC and BIC.
Limitations of using Python for causal inference and econometric analysis include potential complexities in specifying mixed-effects models and interpreting residual diagnostics for non-standard distributions. Users must carefully handle model specification and robust standard errors to avoid invalid econometric conclusions.
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