Learn econometrics
Start with a question, understand the assumptions, then try an analysis. Follow a topic from its first explanation to a method reference and a worked lab. The learning material is free to read.
What would you like to understand?
Each path connects explanations, methods and practice. Start at the beginning or jump to the question you need.
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01 · Learning path
Econometrics
Start with the question you want to answer. Learn how economic reasoning, data and statistical methods fit together before choosing an estimator.
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02 · Learning path
Data analysis
Know what each row measures, explore the data and separate what your sample shows from what you can infer about a wider population.
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03 · Learning path
Regression & inference
A regression table is the beginning of an argument. Learn what its estimates mean, where the uncertainty comes from and which conclusions the design can support.
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04 · Learning path
Causal inference
To ask what a policy changed, you need a credible account of what would have happened without it. Start with that comparison, then examine the assumptions of each design.
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05 · Learning path
Panel & time series
Repeated observations add information and dependence. Choose the route that matches your data: many units followed over time, or a series whose ordering matters.
Start here
Plain-language articles from the blog.
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Basics
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.
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Basics
What is data analysis? A practical introduction with examples
Data analysis turns observations into answers. Learn the steps, common types of analysis and how to turn a question into a defensible conclusion.
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Basics
What is regression analysis? A worked example in Python
Regression analysis describes how the average of one variable changes with others. Learn to fit, read and question a linear regression, step by step.
Practical guides
Workflows, method explainers and validation, from the guides.
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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.
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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.
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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.
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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.
Methods reference
One page per estimator family, with the Stata command it corresponds to, supported options and a runnable example. See all 28 method pages.
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Linear models and IV
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Panel data
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Causal inference
- Difference-in-differences: didregress and parallel trends
- Staggered difference-in-differences: Callaway–Sant'Anna csdid
- Event study regression: leads, lags and pre-trend tests
- Regression discontinuity: rdrobust with robust bias correction
- Treatment effects: propensity score matching, IPW and AIPW
- Synthetic control: donor weights, placebos and synthetic DiD
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Limited and duration outcomes
- Logit and probit regression with marginal effects
- Ordered logit, ordered probit and multinomial logit
- Poisson regression, negative binomial and PPML gravity models
- Tobit model and truncated regression
- Heckman selection model: ML and two-step estimation
- Survival analysis: Cox proportional hazards, stcox and streg
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Time series
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Tests and inference
Teaching labs
Four courses with 80 labs. Each lab pairs an economic question with data and a complete Python analysis.
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20 labs
Econometrics
Regression, policy evaluation and introductory time series.
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20 labs
Statistics
Data, probability, sampling, intervals, tests and reporting.
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20 labs
Microeconomics
Consumer choice, production, markets, strategic interaction and welfare.
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20 labs
Advanced Econometrics
Projection algebra, covariance, panel transformations, IV, likelihood and dynamic responses.
Reference
Definitions and answers you can come back to.
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40 terms
Econometrics glossary
Short, plain-language definitions of common econometrics terms, from autocorrelation to weak instruments, with links to longer explanations.
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11 questions
Frequently asked questions
Answers to common questions about OpenEconometrics: what it costs, which methods and platforms it supports, how accurate it is and where your data goes.
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