Blog
Plain-language explanations of econometrics and data analysis, with concrete examples, sources and links to worked labs. Follow a learning path to read by topic, or browse the latest articles below.
Basics
Core ideas of econometrics, explained from first principles.
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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
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.
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Basics
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.
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Basics
Exploratory data analysis: what to check before fitting a model
Exploratory data analysis checks a dataset's structure, missing values and unusual observations. Follow a practical checklist before choosing a model.
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Basics
What is time series analysis? Patterns, models and forecasting
Time series analysis studies observations recorded over time. Learn about trends, seasonality, stationarity and how to evaluate forecasts without leakage.
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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 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.
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Basics
What is panel data? Fixed effects, random effects and the Hausman test
Panel data follow the same units over time. Learn why that helps with unobserved differences, and how fixed effects, random effects and the Hausman test work.
Causal inference
How economists move from correlation to cause and effect.
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Causal inference
Correlation vs causation in economics: from confounding to natural experiments
Why a regression coefficient is not automatically a causal effect, and how economists use experiments, instruments and natural experiments to find one.
Statistical inference
Uncertainty, tests and how to report them.
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Statistical inference
Descriptive vs inferential statistics: the difference with examples
Descriptive statistics summarise observed data; inference makes claims beyond them. Compare examples, confidence intervals and the assumptions behind each.
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Statistical inference
How to read regression results: coefficients, uncertainty and fit
Read a regression table in context: check the sample and units, interpret coefficients and confidence intervals, compare models and separate fit from causality.
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Statistical inference
P-values in economics: what they mean and what they do not
A p-value measures how surprising the data would be under a null hypothesis. Learn to read it correctly, avoid common mistakes and report uncertainty well.