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linearmodels

by Kevin Sheppard · Library

Python package by Kevin Sheppard adding panel data, instrumental-variable, system (SUR/3SLS) and asset-pricing estimators missing from statsmodels

NCSA licenseFreeOpen sourceLocalSelf-hostable
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Link verified Nov 2, 2026

Overview

linearmodels is a Python library by Kevin Sheppard that fills the gaps in statsmodels for applied econometrics and empirical finance. Its panel module offers pooled OLS, fixed effects (PanelOLS with entity, time and other effects), random effects, between, first-difference and Fama-MacBeth estimators plus AbsorbingLS for high-dimensional effects; the IV module provides 2SLS, LIML, GMM, continuously updated GMM and JIVE with weak-identification and specification tests; system estimation covers SUR, 3SLS and system GMM; and the asset-pricing module implements two-step and GMM linear factor models and a SUR model for traded factors. Clustered, heteroskedasticity-robust and kernel covariance estimators are available throughout, with formula interfaces and pandas MultiIndex panels as inputs.

The package is released under the University of Illinois/NCSA license, follows statsmodels conventions and prints comparable summary tables. Version 7.1 is current at the time of writing. It is single-machine and does not cover nonlinear panel models or dynamic panel GMM (Arellano-Bond), for which R or Stata remain more complete.

Capabilities

  • Panel estimators: pooled, fixed, random, between, first-difference, Fama-MacBeth
  • AbsorbingLS for high-dimensional fixed effects
  • IV: 2SLS, LIML, GMM, CUE-GMM, JIVE with diagnostics
  • System estimation: SUR, 3SLS, system GMM
  • Linear factor asset-pricing models
  • Clustered and robust covariance estimators
  • Formula interface and pandas MultiIndex panels

Typical workflows

  1. Two-way fixed-effects panel regression with clustered errors
  2. Fama-MacBeth cross-sectional asset-pricing tests
  3. IV estimation with weak-instrument diagnostics

Strengths

  • Covers estimators statsmodels lacks
  • Consistent with pandas and statsmodels conventions
  • Permissive NCSA licence

Limitations

  • No dynamic panel GMM (Arellano-Bond)
  • Linear models only
  • Smaller community than statsmodels

Prerequisites

  • Python with NumPy, pandas and statsmodels

Who it is for

  • Empirical finance researchers
  • Applied econometricians working in Python

Alternatives & comparison

statsmodels covers general regression and time series but not panel IV or system models; fixest in R is faster for many-level fixed effects and includes event-study tooling; arch, by the same author, addresses volatility and bootstrap methods.

  • arch (Python package)

    Kevin Sheppard's Python package for univariate ARCH/GARCH volatility models, bootstraps, multiple-comparison procedures, unit-root and cointegration tests

    Open sourceFree
  • fixest

    R package for fast estimation of OLS, GLM, Poisson, IV and difference-in-differences models with many fixed effects, clustered errors, multiple estimations and publication tables

    Open sourceFree
  • Stata

    Commercial statistical software from StataCorp widely used in economics and social science, with editions BE/SE/MP, a command language, Mata and comprehensive PDF manuals

    ProprietaryPaid
  • statsmodels

    Python library for estimating statistical and econometric models with R-style formulas, rich result summaries and tests: OLS, GLM, discrete choice, ARIMA, state space, VAR and more

    Open sourceFree
Compare linearmodels with its alternatives →

Sources & verification

Primary sources consulted for this record. Claims are tied to the fields each source supports; the automated link checker re-verifies every URL on a schedule.

  • License
    linearmodels LICENSE.md

    https://github.com/bashtage/linearmodels/blob/main/LICENSE.md

    Supports: license_spdx, source_model

    University of Illinois/NCSA Open Source License text

    checked Nov 1, 2026
  • Official site
    linearmodels 7.1

    https://bashtage.github.io/linearmodels

    Supports: name, summary, capabilities, vendor

    checked Nov 1, 2026
  • Repository
    bashtage/linearmodels

    https://github.com/bashtage/linearmodels

    Supports: repo_url

    checked Nov 1, 2026
Confidence
High
Reviewed
Nov 1, 2026
Links
Verified · Nov 2, 2026

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