EE628 Time-Series Econometrics
Course Information
Level: Graduate (MA / PhD) | Credits: 3 | Semester: 2/2025
Schedule: Friday 9:00–12:00 | Room: 205
Prerequisite: EE626 (or instructor’s permission)
Software: R / statistical software of choice
Course Description
This course covers topics in time-series econometrics with an emphasis on practical applications using statistical software. Topics span both classical and modern approaches, from univariate stationary models through to data-rich structural VAR and nonlinear extensions.
Topics
| Week | Topic |
|---|---|
| 1–2 | Univariate stationary ARMA models |
| 3–4 | Multivariate stationary time series — Vector Autoregressive (VAR) model |
| 5 | Structural VAR |
| 6–7 | Unit root and cointegration |
| 8–9 | Time series models of heteroskedasticity (ARCH/GARCH) |
| 10 | Multivariate volatility |
| 11 | Nonlinear time series models |
| 12 | Principal components and factor models |
| 13 | Structural VAR analysis in a data-rich environment |
| 14 | Nonlinear structural VAR |
| 15 | Forecasting with big-dependent data |
Course Learning Outcomes
By the end of this course, students will be able to:
- Analyze the assumptions, strengths, and limitations of ARMA and VAR models
- Evaluate stationarity and cointegration properties of economic time series
- Analyze long-run equilibrium relationships using Vector Error Correction Models (VECM)
- Apply heteroskedastic and nonlinear time series models to financial series
- Use principal components and factor models for dimensionality reduction
- Critically assess time series techniques for real-world economic and financial problems
Assessment
| Component | Weight |
|---|---|
| Problem sets | 10% |
| Midterm examination | 35% |
| Final examination | 40% |
| Empirical project | 15% |
The empirical project requires students to apply methods from the course (ARMA, VAR, cointegration, GARCH, structural break) to a dataset of their choice.
Key References
- Hamilton, J.D. (1994). Time Series Analysis. Princeton University Press.
- Martin, V., Hurn, S., and Harris, D. (2013). Econometric Modelling with Time Series. Cambridge University Press.
- Kilian, L. and Lütkepohl, H. (2017). Structural Vector Autoregressive Analysis. Cambridge University Press.
- Shumway, R.H. and Stoffer, D.S. (2016). Time Series Analysis and Its Applications: With R Examples. Springer.
- Kongcharoen, Chaleampong. Time-Series Econometrics. Lecture Notes.
Chaleampong Kongcharoen
Assistant Professor of Economics, Associated Dean on Academic Affairs
I’m an assistant professor of economics at Thammasat University. My research interests are time series econometrics, and empirical macroeconomics.