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gretl is the best free, dedicated econometrics program for Linux. For a broader research ecosystem, choose R; for econometrics embedded in data pipelines and production software, choose Python. Julia, GNU Octave, GNU PSPP and SageMath fill more specialized roles. This distinction matters because only some choices are standalone applications: R, Python and Julia are programming ecosystems that become econometrics platforms through packages.
The recommendations below prioritize econometric coverage, Linux availability, reproducibility, maintenance, interoperability and teaching usefulness. Version references were checked on August 16, 2026 and will change as projects release updates.
Quick comparison
| Software | Best for | GUI | Panel data | Time series | IV/GMM | Main limitation |
|---|---|---|---|---|---|---|
| gretl | Dedicated econometrics and teaching | Yes | Built in | Strong | Built in | Smaller ecosystem |
| R | Academic research and reproducible analysis | Optional | plm, fixest |
Extensive packages | Many packages | Package choices require judgment |
| Python | Econometrics plus data engineering | Optional | linearmodels |
statsmodels |
linearmodels and others |
More programming intensive |
| Julia | Fast numerical and custom research | Optional | Package dependent | Growing ecosystem | Package dependent | Smaller, less mature ecosystem |
| GNU Octave | MATLAB-compatible numerical work | Yes | Package or custom code | Package or custom code | Package or custom code | Not an integrated econometrics suite |
| GNU PSPP | Basic SPSS-style statistics | Yes | Limited | Limited | Limited | Not a modern econometrics replacement |
| SageMath | Symbolic mathematics and custom computation | Notebook interfaces | Python libraries | Python libraries | Python libraries | Econometrics is not its core purpose |
“Free” can mean no payment, while “free software” also concerns rights to inspect, modify and redistribute code. The projects below are open-source or free-software candidates; commercial products such as Stata and EViews are outside this list.
1. gretl: best dedicated Linux econometrics application
gretl is the closest match to a free Linux alternative to EViews or Stata’s conventional econometrics workflow. Its 2026b release was published April 30, 2026, and the project provides Linux packages and source-build instructions.
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What it handles
- Least squares, maximum likelihood, instrumental variables and GMM.
- ARIMA, GARCH, VAR, VECM, structural VAR, unit-root and cointegration tests.
- Panel estimators, dynamic panels, limited-dependent-variable models and Kalman filtering.
- Scripts in the hansl language, LaTeX tables and gnuplot graphs.
- CSV, Excel-compatible, OpenDocument, Stata, SPSS, EViews, JMulTi and RATS formats, with interoperability with R, Python, Julia, Octave and Stata.
Linux installation
On Debian or Ubuntu, the distribution package is usually:
sudo apt update
sudo apt install gretlRepository versions can lag upstream. Use the official Linux packages or source instructions at gretl.sourceforge.net when the newest release matters.
Trade-offs
gretl is approachable and purpose-built, but its community and contributed-package ecosystem are smaller than R’s or Python’s. Specialized methods may require scripts, function packages or exporting data to another language. Verdict: the best true standalone econometrics application for Linux.
2. R: best overall open-source econometrics ecosystem
R is a free statistical-computing environment rather than one desktop econometrics product. The R Foundation lists R 4.6.1, released June 24, 2026. Its strength is the depth of packages for estimation, inference, visualization and reproducible reporting.
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Core packages
lmtestandsandwichfor tests and robust covariance estimators.plmfor within, random-effects, between, first-difference, IV, GMM, FGLS, mean-group, common-correlated-effects and panel limited-dependent-variable models; see CRAN’s plm page.fixestfor high-dimensional fixed effects, OLS, IV, GLM, maximum likelihood, difference-in-differences, clustered inference and publication tables; see its documentation.AER,ivreg,vars,urca,quantreg,forecast,fable,tsDyn,frontierand spatial or survival packages for specialized designs.- Quarto, R Markdown and LaTeX for reports that can be rebuilt from scripts.
Installation and project control
sudo apt update
sudo apt install r-baseinstall.packages(c("plm","fixest","lmtest","sandwich","AER","vars","urca","quantreg"))
install.packages("renv")
renv::init()
renv::snapshot()
R is powerful but fragmented: package defaults, formulas, missing-data rules and degrees-of-freedom corrections can differ. Verdict: the strongest long-term choice for researchers willing to learn code.
3. Python with statsmodels and linearmodels: best programmable workflow
Python is the practical choice when econometrics must share a workflow with databases, APIs, machine learning, automation, dashboards or production services. statsmodels covers OLS, GLS, WLS, GLM, GEE, quantile and robust regression, discrete-choice and count models, ARIMA, VARMA, state-space models and statistical tests. linearmodels adds panel, instrumental-variable, asset-pricing and related estimators; its documentation identifies version 7.0.
Set up an isolated environment
python3 -m venv econ-env
source econ-env/bin/activate
python -m pip install --upgrade pip
python -m pip install numpy pandas scipy statsmodels linearmodels matplotlib jupyterOLS with robust standard errors
import statsmodels.api as sm
X = sm.add_constant(data[["income", "education"]])
model = sm.OLS(data["consumption"], X).fit(cov_type="HC1")
print(model.summary())Do not assume every panel or IV estimator is in statsmodels. Python’s breadth also makes it easy to optimize prediction while overlooking causal identification. Verdict: best when econometrics is one component of a larger software workflow.
4. Julia with Econometrics.jl: best emerging high-performance option
Julia is an open-source language designed for numerical and scientific computing. Econometrics.jl provides econometric estimation and associated statistics, while other Julia packages support regression, optimization, simulation, time series and high-dimensional computation.
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juliausing Pkg
Pkg.add("Econometrics")Julia suits large matrices, simulation and custom estimators, and can interoperate with R and Python. Its econometrics ecosystem remains smaller and less mature than R’s or Stata’s; a 2024 review discusses that gap at arXiv. Pin project environments with Pkg.activate and Pkg.instantiate. Verdict: promising for performance-oriented researchers, not the easiest default.
5. GNU Octave: best free MATLAB-compatible environment
GNU Octave offers a MATLAB-like language, GUI, console and shell use on Linux. Its Statistics package lists version 1.8.4 dated July 10, 2026, with regression, distributions, clustering and descriptive-statistics functions.
sudo apt update
sudo apt install octavepkg install -forge statistics
pkg load statisticsPackage names and availability vary by Octave release and distribution. Octave is not a unified econometrics suite: panel, IV, GMM and advanced time-series work may require additional packages or your own code. MATLAB syntax compatibility is useful, but it does not imply Stata-like econometric breadth. Verdict: a sensible foundation for MATLAB users, not the first choice for beginners.
6. GNU PSPP: best basic SPSS-style alternative
GNU PSPP is a GPL-licensed replacement for many common SPSS workflows. It provides descriptive statistics, t-tests, ANOVA, linear and logistic regression, association measures, clustering, reliability and factor analysis, non-parametric tests and PDF, PostScript, OpenDocument, HTML and text output. The GNU project reports PSPP 2.1.1.
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sudo apt update
sudo apt install psppPSPP has no time limit, artificial case or variable limits, or separately purchased advanced-function packages. Its limitations are decisive for econometrics: advanced time series, panel data, IV, GMM, causal designs and state-space models are not its strengths. Verdict: useful for introductory regression and SPSS compatibility, not a full Stata, gretl or EViews replacement.
7. SageMath: best broad mathematical platform
SageMath combines symbolic algebra, numerical computation, linear algebra and optimization in an open-source system that can use Python scientific libraries. It is valuable for theoretical economics, simulation and custom mathematical research.
Econometric estimators generally come from Python libraries rather than SageMath itself. Setup is more complex than installing Python directly, so applied-econometrics users will usually find R, Python or gretl more efficient. Verdict: a powerful specialist environment, not a conventional econometrics program.
Which Linux software should you choose?
- Beginner or instructor needing menus: start with gretl.
- Graduate researcher or academic author: choose R, especially
plmandfixest. - Analyst or engineer building pipelines: choose Python with statsmodels and linearmodels.
- Computational economist implementing new methods: evaluate Julia.
- Experienced MATLAB user: use GNU Octave, adding packages as needed.
- Basic SPSS-style coursework: use PSPP.
- Symbolic mathematics or optimization first: consider SageMath and Python libraries.
Install a practical baseline on Debian or Ubuntu
The following is distribution-dependent and may install older repository versions:
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sudo apt update
sudo apt install gretl r-base python3 python3-pip python3-venv octave psppJulia and SageMath installation methods vary more by distribution; consult their official project pages. For current gretl, R, Python-package, Julia or Octave features, prefer upstream documentation over assuming the repository is current.
Make the analysis reproducible
- Record your Linux distribution, architecture, software versions and package versions.
- Keep raw data separate from processed data and preserve a data dictionary.
- Store scripts and configuration in Git.
- Pin dependencies: use
renvfor R, a requirements or lock file for Python, and Julia project environments. - Generate tables and figures from scripts instead of copying output by hand.
- Report sample restrictions, missing-data rules, transformations, estimator, covariance estimator and clustering level.
What software cannot decide for you
Every listed platform can calculate an estimate without proving that the estimate answers your question. You must still assess exogeneity, instrument relevance and exclusion, parallel trends, stationarity, dynamic specification, clustering, weak identification, attrition, selection, measurement error and multiple testing. Default standard errors are not automatically appropriate, and prediction accuracy is not evidence of causal identification.
Also compare package defaults before combining results: missing-data handling, reference categories, covariance corrections, optimization methods and degrees of freedom may differ across R, Python and Julia packages.
The Bottom Line
Choose gretl for the best free dedicated Linux econometrics GUI, R for the broadest research ecosystem, and Python when econometrics must integrate with data engineering and software systems. Julia, Octave, PSPP and SageMath are worthwhile when their specific workflow advantages outweigh their narrower econometric coverage.
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