A guided reading path
Econometrics: a beginner’s guide
Start with the question you want to answer. Learn how economic reasoning, data and statistical methods fit together before choosing an estimator.
01 · Understand the question
Build the foundations
Read in this order, or open the question you are working on.
What is econometrics? A plain-language introduction
Econometrics uses data and statistical methods to measure economic relationships, test theories and evaluate policies. Here is what that means in practice.
Econometrics vs statistics: how they differ and overlap
Econometrics and statistics share methods. Compare their questions, assumptions and uses, with an example that separates prediction from a causal effect.
What is econometric analysis? From question to evidence
Econometric analysis connects an economic question to data, a model and defensible conclusions. Follow a practical workflow with a policy-evaluation example.
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.
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.
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.
02 · Examine the assumptions
Explore methods when you need them
A clear description may already answer your question. If modelling is useful, these references explain the estimator, supported options, a runnable example and limitations. Choose one for your question and assumptions.
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Linear models and IV
OLS regression in Python with robust and clustered standard errors
Fit OLS and weighted least squares in Python with HC0–HC3, clustered or HAC standard errors, then run tests, linear combinations and margins.
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Linear models and IV
Instrumental variables in Python: 2SLS, LIML and GMM
Estimate instrumental-variables regressions in Python with 2SLS, LIML or GMM, read the weak-instrument and overidentification tests, and map them to ivregress.
03 · Work through the evidence
Try a worked lab
These exercises use synthetic teaching data. The full handout, Python analysis and available resources are linked on each lab page.
Worked lab
Turn an economic question into a model
Define the outcome, units and comparison before estimating a relationship.
Worked lab
Build a complete research report
Bring estimates, uncertainty and a reproducible account of the analysis together.
Try it on your own data
OpenEconometrics is free and open source under Apache 2.0. Install the Python library or download the desktop alpha.
pip install openecon