Course overview
This course is designed to deepen students’ critical thinking, analytical reasoning, and mathematical problem-solving skills in the context of time series econometrics. It covers key concepts and techniques such as stationarity, unit roots, autoregressive moving average (ARMA) models, forecasting, model selection criteria (including machine learning methods), and (structural) vector autoregression (SVAR), including the identification of structural shocks. Emphasis is placed on developing a solid understanding of both the theoretical foundations and the practical implementation of these methods. Through a combination of lectures, applied exercises, and real-world data analysis, students will gain the ability to critically evaluate and apply time series models to empirical economic questions and policy-relevant problems.
- Time Series Regressions
- ARIMA Models
- Vector Autoregression Models
Course learning outcomes
- Demonstrate knowledge in various advanced time series econometric methods, estimation methods and related econometric theories.
- Apply these methods to empirical data or develop new time series econometric methodologies.
- Use specialised software to estimate time series econometric models using real world data.
- Engage in collaborative work on empirical time series projects aimed at addressing key issues in applied or theoretical time series econometrics.