Econometrics is statistics applied to questions economists actually care about. Most of those questions are causal: does raising the minimum wage reduce employment? Does a 1pp interest-rate hike cool inflation? Does microcredit lift household consumption?
Pure statistics gives you correlations. Econometrics gives you a framework for turning correlations into defensible causal claims — and, just as importantly, for spotting when a correlation cannot be safely interpreted as cause.
The three things you can ask of data
- Description: what is the average loan size in this SACCO? (Easy. Just compute it.)
- Prediction: given a member's history, how likely are they to default? (Hard, but well-defined. Optimise out-of-sample fit.)
- Causation: if we raised the loan limit, how would defaults change? (Hardest. Requires assumptions about what would have happened in a world that didn't occur.)
Why causation is hard
You never observe the same person under both treatment and control. The 'counterfactual' — what would have happened — has to be inferred. Every credible econometric method is a strategy for constructing that counterfactual.
The fundamental problem of causal inference
For person i, define Y₁ᵢ as their outcome with treatment, Y₀ᵢ without. The causal effect on i is Y₁ᵢ − Y₀ᵢ. We never see both for the same person. So we estimate average effects across a population, where matching treatment and control groups gives us a credible counterfactual.
Random assignment (the gold standard) makes the two groups identical in expectation, so any post-treatment difference must be due to treatment. Most economic data is non-experimental — that's where the methods in this course do their work.
What this course covers
- Linear regression: the workhorse, properly understood
- Endogeneity: the four ways your regression coefficient ends up wrong
- Instrumental variables: the cleanest correction, when you can find one
- Difference-in-differences: leveraging policy variation across groups and time
- Panel data: fixed effects and within-variation
- Time series: why monthly data needs different machinery
- Limited dependent variables: when y is binary or censored
- Reading published empirical work critically
What you don't need
You don't need calculus beyond derivatives, matrix algebra beyond the basics, or statistical software beyond a willingness to learn one (R, Python's statsmodels, or Stata).
Exercise
Look up the Card-Krueger 1994 minimum wage paper. In one sentence, what counterfactual did they construct, and how?