{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"}],"enrichment":{"capability":"Provides panel regression, instrumental variable estimators, system regression, and factor asset pricing models that extend statsmodels for econometric and financial analysis.","skillfed_tags":["econometrics","panel-data","instrumental-variables"],"use_cases":["Estimate panel regression models with entity and time fixed effects on longitudinal firm or country data.","Perform instrumental variable regression when regressors are endogenous, using 2SLS or GMM estimators.","Implement Fama-MacBeth cross-sectional regression for asset pricing factor models across time periods.","Fit system regression models (SUR, 3SLS) when multiple equations share disturbances or have cross-equation constraints.","Handle high-dimensional fixed effects using absorbing least squares for large categorical variables.","Estimate k-class or continuously-updating GMM models for robust inference under weak instruments."],"what_it_does":"Linearmodels extends statsmodels with specialized econometric and financial regression estimators. It provides panel data models (fixed effects, first difference, between, pooled, Fama-MacBeth), instrumental variable methods (2SLS, LIML, k-class, GMM), high-dimensional regression via absorbing least squares, factor asset pricing models, and system regression (SUR, 3SLS, GMM system). The package is designed to work with NumPy, Pandas, and xarray data structures and supports formula-based model specification using formulaic.\n\nThe package targets econometricians, financial researchers, and data scientists working with panel data, endogenous regressors, or multi-equation systems. It builds on statsmodels' foundation but adds specialized estimators not available in the base library. Installation requires seven runtime dependencies (numpy, pandas, scipy, statsmodels, mypy_extensions, pyhdfe, formulaic), and the package is actively maintained with support for current Python versions.","worth_installing":"Yes, with conditions. Install if you need specialized econometric estimators (panel models, IV, system regression, asset pricing) beyond statsmodels' scope and can work with Python 3.10 or later. The package is actively maintained with no known vulnerabilities. Verify the NCSA license terms match your project's requirements before committing to production use."},"id":"linearmodels","links":{"html":"https://skillfed.io/packages/linearmodels","md":"https://skillfed.io/packages/linearmodels.md","pypi":"https://pypi.org/project/linearmodels/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-10-21","license_spdx":"NCSA","license_treatment":"unclear","name":"linearmodels","python_support":"supports_current","summary":"Linear Panel, Instrumental Variable, Asset Pricing, and System Regression models for Python"},"popularity":{"monthly_downloads":440109,"position":6648,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"7.0"}