Shanghai Jiao Tong University · Summer School 2026
Financial Econometrics
The econometrics of finance, taught through models and real data with Stock & Watson (4th ed.) as the main text. How to estimate a relationship, when it is causal rather than merely predictive, and how to model and forecast financial time series, from returns and risk to the term structure. Every method is taught twice, in Stata and in Python.
Course materials
Course overview
Econometrics turns data into evidence. The course builds from the ground up: the probability and statistics behind every estimate; the linear regression model and how to read its output; inference with the right standard errors; the omitted-variable problem that separates a correlation from a causal effect; and the tools of panel data and time series that finance relies on.
The second half is financial time series: how to forecast a series and why stock returns resist it (the efficient-market hypothesis), how a shock propagates through the economy (dynamic causal effects), and how to model risk itself with ARCH/GARCH, several series at once with a VAR, and the long-run links between non-stationary series with cointegration. Throughout, two questions are asked of every estimate: is it precise, and is it causal?
Every lecture comes with a shareable lab, run on real financial and macro data, in both Stata and Python.
Learning outcomes
- read and interpret regression output: coefficients, standard errors, t-statistics, p-values, confidence intervals and R²;
- distinguish prediction from causation, and state the exogeneity assumption E(u | X) = 0 that a causal claim requires;
- diagnose and sign omitted-variable bias, and choose the right standard error — heteroskedasticity-robust, clustered (panel), or HAC (time series);
- estimate and test multiple regressions, including joint hypotheses with the F-test;
- analyse panel data with entity and time fixed effects and clustered standard errors;
- model and forecast financial time series: autoregressions, the random walk and the efficient-market hypothesis, dynamic causal effects, and volatility (ARCH/GARCH), VAR and cointegration;
- work fluently in both Stata and Python, reproducing every analysis on real data.
At a glance
| Day | Session | |
|---|---|---|
| Week 1: Foundations & the regression model | ||
| Mon 29 Jun | L1 · Economic Questions and Probability | |
| Tue 30 Jun | L2 · Review of Statistics | |
| Wed 1 Jul | L3 · Linear Regression with One Regressor | |
| Thu 2 Jul | Quiz 1 (L1–3) | |
| Fri 3 Jul | L4 · Hypothesis Tests and Confidence Intervals | |
| Week 2: Inference, panel data & into time series | ||
| Mon 6 Jul | L5 · Multiple Regression | |
| Tue 7 Jul | L6 · Hypothesis Tests in Multiple Regression | |
| Wed 8 Jul | Quiz 2 (L4–6) | |
| Thu 9 Jul | L7 · Panel Data and Fixed Effects | |
| Fri 10 Jul | Midterm Examination (L1–6) | |
| Week 3: Financial time series | ||
| Mon 13 Jul | L8 · Time Series and Forecasting | |
| Tue 14 Jul | Quiz 3 (L7–8) + Presentation Information | |
| Wed 15 Jul | L9 · Dynamic Causal Effects · L10 · Volatility, VAR and Cointegration | |
| Thu 16 Jul | Group Project Presentations | |
| Fri 17 Jul | Final Examination (L7–10) | |
