Guides
Practical notes on open-source econometrics: how the workflow fits together, how the methods work, and how to check results. Guides distinguish conceptual explanations from runnable examples and link their sources.
Workflows
Moving econometric work into open-source Python, from the first data import to the final table.
-
Workflows
Open-source econometrics in Python for Stata, EViews and SPSS users
What an open-source Python workflow for econometrics looks like, which familiar tasks OpenEconometrics covers today and where its limits are.
Methods
How estimators and inference options work, with references and code you can run.
-
Methods
Robust and clustered standard errors in Python, from HC0 to the wild bootstrap
How HC0–HC3, cluster-robust and HAC standard errors work, when each is appropriate, and why a handful of clusters calls for the wild cluster bootstrap.
Reproducibility
Checking results against independent references and making analyses others can rerun.
-
Reproducibility
How to validate econometric software results, starting with NIST Longley
Check estimator results against certified reference data, measure accuracy in correct digits, and know what a passing test does and does not prove.
-
Reproducibility
Reproducible econometrics: from raw data to a table you can rerun
A practical workflow for econometric analyses that others can rerun, with pinned versions, recorded samples, seeds and tables generated from saved results.