Professional-grade open-source econometrics for rigorous statistical modeling and time-series forecasting.
gretl (Gnu Regression, Econometrics and Time-series Library) is a high-performance statistical package written in C, designed to provide a sophisticated yet accessible platform for econometric analysis. In the 2026 landscape, gretl remains a critical 'ground truth' engine for researchers and data scientists who require transparent, reproducible statistical modeling that proprietary AI platforms often obscure. Its architecture is built on a modular GTK+ framework, allowing for high-speed execution of complex matrix operations and large-scale regressions. Unlike typical black-box ML tools, gretl offers a unique combination of a user-friendly GUI and a powerful scripting language called 'hansl' (highly accessible network statistics language). This allows users to transition seamlessly from point-and-click exploration to automated, high-volume data pipelines. It natively supports a vast array of econometric estimators, including OLS, GMM, Maximum Likelihood, and VAR/VECM models. Its position in 2026 is solidified as the open-source alternative to Stata and EViews, serving as a vital bridge between traditional statistical theory and modern computational data science, specifically in fields like macro-economic forecasting, financial volatility analysis, and policy impact assessment.
A Turing-complete, matrix-oriented scripting language designed for econometrics, allowing complex loop structures and custom function packages.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Native implementations of Generalized Method of Moments and Likelihood estimation with flexible constraint specifications.
Integrated tools for Vector Autoregressions and Vector Error Correction Models, including impulse response functions and variance decompositions.
Automatic conversion of statistical model outputs into publication-quality LaTeX tables and equations.
Ability to utilize multiple CPU cores for heavy-duty simulations (Monte Carlo methods) and bootstrapping.
Native hook into the US Census Bureau's seasonal adjustment software for sophisticated economic data cleaning.
A community-driven repository of specialized econometric modules (e.g., MIDAS, Quantile Regression) that can be installed on-demand.
Central banks need to predict CPI changes using multiple lagged variables and structural breaks.
Registry Updated:2/7/2026
Forecast 4 quarters ahead.
Financial analysts need to model 'volatility clustering' in equity returns.
Appraisers need to estimate house prices while controlling for spatial and qualitative attributes.