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statsmodels

Estimate regression models and run time series diagnostics with statsmodels.

1|Updated Mar 20, 2026
Search Tags:#time series#diagnostics#statistics#regression#econometrics#model validation
One-click install
npx skills add https://github.com/jadzoghaib/Sabadell_Capstone --skill statsmodels-jadzoghaib
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statsmodels
Source: https://github.com/jadzoghaib/Sabadell_Capstone/tree/main/.claude/skills/statsmodels
Command: npx skills add https://github.com/jadzoghaib/Sabadell_Capstone --skill statsmodels-jadzoghaib

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, statsmodels, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides comprehensive tools for estimation, inference, and diagnostics of statistical models, resolving complex analytical problems in econometrics, time series, and regression analysis.

Core Features & Use Cases

  • Estimate and interpret regression models including OLS, WLS, GLS, and mixed effects models to analyze data patterns across social sciences, finance, and economics.
  • Perform time series analysis such as ARIMA, SARIMAX, and VAR models to forecast economic indicators or financial data with confidence intervals.
  • Diagnostic and hypothesis testing for residuals, heteroskedasticity, autocorrelation, and influential points to validate model assumptions and improve robustness.
  • Use Case: An economist models GDP growth with ARIMA, checks residual diagnostics, and generates forecasts for policy analysis.

Quick Start

Use the statsmodels library to fit an ordinary least squares regression on your dataset and interpret the summary output to evaluate model significance and assumptions.

Frequently Asked Questions about statsmodels

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform time series forecasting with ARIMA and SARIMAX models?▼

Time series forecasting with ARIMA and SARIMAX models is performed by fitting statistical models to your dataset to project economic or financial indicators and generate complete forecasts with confidence intervals.

What is the best way to run diagnostic tests for heteroskedasticity and autocorrelation in regression models?▼

Diagnostic testing for heteroskedasticity and autocorrelation involves checking residuals and influential points to validate model assumptions, ensuring the robustness of OLS, WLS, and GLS regression analyses.

Can I estimate econometrics models like OLS and GLS using numpy and pandas data?▼

You can estimate econometrics models like OLS and GLS directly using numpy and pandas data, requiring these libraries for data manipulation before applying model estimation techniques.

How do I interpret summary output after fitting an ordinary least squares regression?▼

Interpreting OLS regression summary output requires evaluating model significance and diagnostic metrics to analyze data patterns and validate underlying statistical assumptions across economics and finance datasets.

Does this approach support mixed effects models for complex datasets in scientific research?▼

The approach supports mixed effects models for analyzing complex datasets in scientific research, providing detailed estimation and inference tools designed specifically for rigorous statistical modeling and econometrics analysis.

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