Top 10 Best Econometric Software of 2026

SAS Econometrics covers mainstream econometric tasks like regression-based inference, model specification checks, and estimation for time-series and panel structures using SAS estimation procedures. The output layer is tightly integrated with SAS results objects, so downstream reporting and automated table generation can reuse the same run outputs. Reproducible estimation scripts can capture data steps, transformations, and estimation calls in one executable flow.

A tradeoff is that the SAS programming model and the project execution pattern increase setup time compared with lighter tools like Gretl and many Python-only workflows. SAS Econometrics fits best when an organization already standardizes on SAS for data preparation and when governance needs favor scripted runs over manual clicking. It also fits teams that need consistent regression output formatting across many studies.

Python is a strong choice for econometric work that needs repeatable estimation pipelines across changing datasets. Core capabilities come from widely used scientific libraries that provide regression engines, optimization routines, and statistical utilities, plus package-level support for time-series and panel modeling workflows. Outputs can be captured programmatically into tables and figures, which helps when results must be regenerated for each specification run. The ecosystem also supports reading and writing common data formats so data preparation stays in the same codebase as estimation.

Tradeoffs show up in coverage gaps and dependency choices, since many econometric models require specific third-party packages. A typical usage situation is dynamic specification development where regression functions, robust or clustered covariance options, and plot generation are driven by configuration in scripts. This setup works best when the team can standardize package versions and build a consistent notebook or script template.

Gretl provides an integrated estimation environment with wizards for importing data, specifying models, and generating regression output tables. It also includes a scriptable workflow for reproducible estimation runs, which is useful when models must be rerun after data cleaning changes. The tool’s built-in command set reduces dependence on external packages for many standard econometric tasks.

A key tradeoff is narrower integration with enterprise data pipelines than Python-based stacks, since Gretl scripting and file-based data handling do not replace SQL-centric ETL. Gretl fits well when analysis needs to be repeatable on a single analyst machine, such as a course or research group that standardizes model scripts and output formatting.

EViews is econometric software built for fast interactive work on time-series, cross-sectional, and panel datasets. It pairs a dedicated scripting language with point-and-click workflows for estimation, diagnostics, and repeatable output tables.

Core strengths include tightly integrated model objects, specification testing routines, and exportable results for reports. The environment also supports automation via scripts and batch runs for workflows that must rerun consistently.

OxMetrics runs econometric estimation through a GUI workflow tied to reusable scripts for repeatable analysis.

It supports time-series econometrics, panel and limited dependent variable models, and specification testing with exportable regression output tables.

Automation is handled by batch runs that reuse saved model setups and produce consistent outputs across iterations.

Statsmodels fits teams that need econometric methods embedded in a Python workflow and reproduced from scripts, not point-and-click results. It provides a broad set of regression and statistical modeling APIs, including time-series estimators and hypothesis tests alongside conventional OLS and GLM.

The library returns structured results objects with consistent attributes for coefficients, standard errors, diagnostics, and prediction outputs. Many workflows stay fully in Python, with formulas support, stats-focused plotting utilities, and estimators designed for iterative model fitting and comparison.

Stata is an econometrics-first statistics package that mixes a dedicated workflow with a programmable command language. It supports cross-sectional, panel, and time-series econometrics through built-in estimators and a large ecosystem of contributed commands.

Reproducible estimation is driven by do-files that keep data preparation, estimation, and regression tables in one script. Output can be exported for reporting, and estimation results can be post-processed through its return system after each model run.

GAUSS delivers econometric modeling through a dedicated statistical programming language and a large set of built-in estimation procedures. It supports workflows for maximum likelihood estimation, limited dependent variable modeling, and time-series toolchains such as state-space filtering.

Output formatting for regression tables, log files, and reproducible estimation scripts is geared toward repeatable research runs. The main distinction is that modeling tasks run inside the GAUSS language rather than through point-and-click notebooks alone.

Julia runs econometric estimations by compiling model code to native machine performance and executing it through a high-level statistical programming workflow. Its core capabilities include maximum likelihood, constrained and nonlinear estimation, instrumental variables workflows, and reproducible regression scripts with structured outputs for tables.

The package ecosystem supports time-series analysis paths such as state-space modeling and Kalman filtering, plus forecasting components like impulse-response and variance decomposition in compatible toolchains. Julia can integrate with external data sources and lets teams wrap estimation routines behind clean APIs for automation across experiments and batches.

SHAZAM is a focused econometrics workspace used for estimation, diagnostics, and reporting from a command-driven workflow. It is distinct for strong built-in econometric modeling coverage such as limited dependent variable models, time-series inference routines, and equation-by-equation estimation for common econometric structures.

The typical workflow emphasizes reproducible estimation scripts that generate regression output tables without relying on external statistical notebooks. For many teams, SHAZAM is the faster path when modeling is the priority and when the workflow needs to stay inside one econometrics tool rather than stitching together code and add-ons.