MB634 Business, Finance, and Economic Analysis and Forecast
Course Information
Level: MBA (Master of Business Economics) | Semester: 1/2568
Schedule: Sunday 13:00–16:00 | Room: Computer Room, Floor 4, Faculty of Economics (Tha Prachan)
Software: EViews / Stata / Python
Teaching Format: Flipped Classroom with hands-on computer sessions
Course Description
This course studies statistical methods and time series models for analyzing and forecasting business and economic variables at both micro and macro levels. Topics include demand and supply modelling, GDP growth forecasting, inflation and interest rate forecasting, exchange rate analysis, and identification of key economic factors driving these variables. Students apply software tools to real-world forecasting problems.
Topics
| Week | Topic |
|---|---|
| 1 | Introduction to forecasting: events, information sets, uncertainty, error measurement |
| 2 | EViews for basic data analysis and graphing |
| 3 | Cross-sectional data forecasting with regression |
| 4 | Trend and seasonal forecasting |
| 5–6 | Cycle forecasting: AR and MA models, ARMA |
| 7 | Time-varying volatility: ARCH and GARCH models |
| 8 | Nonstationary time series: random walk, unit root tests, ARIMA |
| 9 | Cointegration and Error Correction Model |
| 10 | Multivariate time series: VAR model, Granger causality, Impulse Response Function |
| 11 | Forecast evaluation: point, interval, and density forecasts |
| 12 | Forecast combination: model-based, market-based, and survey-based |
| 13 | Nonlinear models and structural break models |
| 14 | Variable selection: information criteria, stepwise, shrinkage, principal components |
| 15 | Machine learning for forecasting |
Course Learning Outcomes
By the end of this course, students will be able to:
- Apply statistical software to business and economic forecasting problems
- Evaluate model performance and forecasting accuracy
- Present forecasting results in a clear and professional format
Assessment
| Component | Weight |
|---|---|
| In-class activities (10 of 12 sessions) | 15% |
| Mini homework assignments (10 sets) | 20% |
| Forecasting report (individual) | 15% |
| Midterm examination | 20% |
| Final examination | 30% |
Forecasting report requirements:
- Introduction describing the chosen time series data and objectives
- Model selection methodology with comparison across candidate models (MAE, MSE, AIC, BIC, cross-validation)
- Presentation of chosen model and parameter estimates
- Out-of-sample forecast for 1 year ahead (point and interval forecasts)
Key References
- Diebold, F.X. (2024). Forecasting in Economics, Business, Finance and Beyond. Free online
- Hyndman, R.J. and Athanasopoulos, G. Forecasting: Principles and Practice. 3rd ed. Free online
- Kongcharoen, Chaleampong. เศรษฐมิติทางการเงินเบื้องต้น. 2nd ed. Thammasat University Press, 2567.
- Ghysels, E. and Marcellino, M. (2018). Applied Economic Forecasting Using Time Series Methods. Oxford University Press.
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.