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ols-regression

Estimate OLS regression models with robust standard errors and econometric diagnostics.

33|6|Updated Mar 17, 2026
Search Tags:#econometrics#causal-inference#statsmodels#ols#vif#regression diagnostics#robust standard errors
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill ols-regression-xjtulyc
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ols-regression
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/07-economics/ols-regression
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill ols-regression-xjtulyc

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires statsmodels>=0.14.0, pandas>=2.0.0, numpy>=1.24.0, scipy>=1.10.0, matplotlib>=3.7.0, seaborn>=0.12.0, patsy>=0.5.3.

What problem does it solve?

This Skill helps you perform ordinary least squares regression and avoid misleading inference by running key econometric diagnostics and producing presentation-ready results.

Core Features & Use Cases

  • Full OLS workflow: fit models from a Patsy formula, generate results, and compute fit statistics (N, R², adjusted R², AIC/BIC, F-statistic).
  • Diagnostics for validity: test heteroscedasticity (Breusch–Pagan and White), check functional form misspecification (RESET), assess residual normality, and report Durbin–Watson for autocorrelation signals.
  • Robust inference & reporting: compute HC3 robust standard errors and VIF for multicollinearity, optionally support clustered SE, and generate regression tables suitable for write-ups.
  • Use case: analyze how education and experience relate to log wages while reporting heteroscedasticity-aware standard errors, VIF flags, and diagnostic plots to support an economics-style empirical paper.

Quick Start

Use the ols-regression skill to estimate an OLS model from your dataset and return robust regression results with heteroscedasticity tests, VIF, RESET, and a publication-style summary table.

Frequently Asked Questions about ols-regression

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

FAQPage Schema
How do I run OLS regression in Python with robust standard errors and heteroscedasticity tests?▼

Run OLS regression with robust standard errors by fitting a Patsy formula to a pandas DataFrame, which computes HC3 or clustered covariance and runs Breusch-Pagan and White tests for heteroscedasticity. This ensures reliable econometric inference for empirical analysis.

How do I check multicollinearity and model specification when running OLS regression?▼

Check multicollinearity and model specification by calculating Variance Inflation Factor (VIF) for predictors and running a RESET test for functional form misspecification. These diagnostics validate the OLS model specification and flag problematic independent variables.

What's the best way to generate publication-ready regression tables from statsmodels?▼

Generate publication-ready regression tables from statsmodels by estimating OLS models and exporting formatted summary outputs with fit statistics like R-squared, adjusted R-squared, AIC, and BIC. This produces research-paper-quality results with robust standard errors.

Can I use Patsy formulas with a pandas DataFrame to estimate econometric models in statsmodels?▼

You can use Patsy formulas with a pandas DataFrame to estimate econometric models in statsmodels by defining the dependent and independent variables in a string formula. This requires pandas and statsmodels dependencies to parse the formula and fit the OLS model.

Why do my OLS regression standard errors change when I apply robust covariance adjustments?▼

OLS regression standard errors change with robust covariance adjustments because HC3 or clustered standard errors correct for heteroscedasticity detected by Breusch-Pagan and White tests. This provides valid inference even when residual variance is not constant.

When should I use clustered standard errors instead of HC3 robust standard errors in OLS regression?▼

Use clustered standard errors instead of HC3 robust standard errors in OLS regression when data has grouped or panel structures, as clustered covariance accounts for within-group correlation. HC3 is appropriate for general heteroscedasticity without group dependencies.

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